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第一轮分析工作暂存

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## 目标
当前分析的核心不是再证明控制有效，而是识别 **控制实际作用于哪些流动结构通道**，并把黑箱控制关系推进成可解释的结构关系：
\[
\text{obs} \rightarrow z \rightarrow \text{act} \rightarrow \text{wake structure} \rightarrow \text{signature}
\]
在当前 pinball 问题中，控制器依赖各圆柱受力与下游三点速度观测，目标是在无扰动来流下实现 stealth 与 illusion。由于任务指标本质上围绕下游传感器信号定义，仅按能量排序的 POD 不足以回答“哪些结构真正与控制和感知结果相关”。CCD 按与 observable 的互相关强度排序模态，适合提取与动作、受力或目标 signature 最相关的结构 [Lyu23]。公共 POD 则提供跨 case 可比的统一坐标系 [Den18b, Den20]。
第一版方案采用 **POD-reduced CCD**：先构造参考 POD 基底，再在低维系数空间里做 CCD。这不是逐式复现 [Lyu23]，而是受其思想启发的 reduced implementation，目标是提升稳健性、降低样本需求，并保证不同 case 在同一坐标系中比较。
## 当前阶段的定位
Round 1 和 Round 2 已经证明了以下几点：
- phase-based reduced CCD 的数据管道可以跑通
- 周期检测、相位重采样、POD、CCD 和指标汇总可以稳定实现
- 真实 illusion PPO replay 必须完整复现 legacy 环境中的 target 谐波、norm 计算和 FIFO 初始化，否则推理无效
- 在简单周期尾迹上，CCD pipeline 是自洽的，但当前结果仍属于 **机制线索**，而不是最终机制结论
因此，下一轮工作的目标不是再证明代码能跑，而是补齐对比链条中缺失的关键参照，尤其是：
1. target case 的 force 数据
2. reference POD basis 的稳定性
3. force / action / signature 三类 CCD 的跨 case 比较
4. cloak 稳态线的完整恢复指标
## 口径与 case 定义
### Reynolds 数口径
项目中存在两套 Reynolds 数定义，后续所有脚本、文件名、图注和表格都必须显式区分，不能混写。
| 记号 | 特征长度 | 含义 |
|---|---|---|
| \(Re\) | 两倍 pinball 单圆柱直径 | 与目标 2D 圆柱比较时常用的口径 |
| \(Re_D\) | pinball 单个圆柱直径 | pinball 本体口径 |
本实施说明中的当前工作范围固定为：
- **无扰动来流**
- **固定 \(Re=100\)**
- 若需换算到 \(Re_D\)，必须在元数据中单独写明
### 当前处理的 case
| case | 流动类型 | 目标 | 当前角色 |
|---|---|---|---|
| uncontrolled | 无控制 pinball 自然尾迹 | 基线 | 周期辅助 case |
| cloak | 受控 pinball | 槽道基流 | 稳态主分析对象 |
| illusion | 受控 pinball | 2D 圆柱在 \(Re=100\) 下的目标尾迹 | 周期主分析对象 |
| target channel | 槽道基流 | 自身 | cloak 参考 |
| target cylinder | 2D 圆柱尾迹 | 自身 | 周期参考 |
具体说明：
- **cloak 的 target** 是槽道基流
- **illusion 的 target** 只考虑 2D 圆柱、\(Re=100\) 的目标尾迹
- 周期线的参考频率与参考相位由 **target cylinder** 定义
## 分析总路线
### 周期线
用于：
- target cylinder
- illusion
- uncontrolled（若通过 relaxed 门）
目标是回答：
- illusion 是否重组了与 target 周期 signature 最相关的结构
- force-related structures、action-related structures 与 signature-related structures 是否对齐
- illusion 是否比 uncontrolled 更接近 target 的相关结构通道
### 稳态线
用于：
- cloak
- target channel
目标是回答：
- cloak 是否把均值尾迹重构为接近槽道基流
- cloak 是否显著压低脉动和回流区
- cloak 的控制幅值与感知改善之间是否具有合理对应关系
## 数据要求
### 必须导出的数据
| 数据组 | 记号 | 说明 |
|---|---|---|
| 流场快照 | \(u(x,y,t_n), v(x,y,t_n)\) | 全域 2D 快照 |
| 总力 | \(F_x(t_n), F_y(t_n)\) | 所有周期 case 必须记录，包括 target cylinder |
| 动作 | \(\Omega_i(t_n)\) | 三圆柱各自转速 |
| 观测 | \(s(t_n)\in\mathbb R^6\) | 三个传感器各含 \(u,v\) |
| 目标观测 | \(s_{tar}(t_n)\) | illusion 时需要；cloak 时为槽道基流参考 |
| 元数据 | `meta.json` | Re 口径、采样步长、case 标签、steady/periodic 标签 |
### 当前不要求的数据
| 数据 | 说明 |
|---|---|
| 单圆柱力矩 \(M_{z,i}\) | 当前阶段不记录 |
| 单圆柱受力分解 | 当前阶段不要求 |
| 控制功率 | 因缺少力矩，当前阶段不做 |
## 采样原则的关键修正
这是本轮最重要的修正之一。
### 不再把 \(T_{ref}\) 的“样本数”当成固定采样标准
`target cylinder` 在某一次采样设置下测得的 \(T_{ref}\approx 18.75\) 样本/周期，只反映当时的 `SAMPLE_INTERVAL`，**不应该被当成所有 case 的固定原始采样密度标准**。
真正固定的只有两件事：
1. **参考频率 / 参考周期**
\[
f_{ref},\qquad T_{ref}=1/f_{ref}
\]
2. **标准相位网格**
\[
\phi_m = 2\pi m/24,\qquad m=0,1,\ldots,23
\]
也就是说：
- \(T_{ref}\) 用来定义统一相位坐标系
- **原始采样频率应按每个 case 自适应设置**，目标是让每个 case 在原始数据里就尽量接近 24 点/周期，而不是先粗采样再强行插值到 24
### 当前采样策略
下一轮对每个周期 case 采用 **两步式采样**。
#### Step 1
传感器先导采样
先用较轻的数据模式跑一个 pilot：
- 高频保存传感器、总力、动作
- 不急着保存全场
- 估计该 case 的 \(f_{case}\)、\(T_{case}\)、\(\mathrm{CV}_T\)
#### Step 2
按 case 自适应设置场采样间隔
对于每个通过周期门的 case，设置该 case 的场采样间隔，使其原始场数据满足：
\[
N_{raw/cycle} \approx 20\text{--}24
\]
推荐用：
\[
\text{SAMPLE\_INTERVAL}_{field} \approx T_{case}/24
\]
若只能取整数步长，则选择最接近 24 点/周期的整数。
### 原始采样密度门槛
为了避免对过稀疏的原始数据做过强插值，定义每个 case 的原始每周期样本数：
\[
N_{raw/cycle} = T_{case}/\text{SAMPLE\_INTERVAL}_{field}
\]
并采用如下门槛：
| 等级 | 条件 | 处理 |
|---|---|---|
| ideal | \(N_{raw/cycle} \ge 20\) | 直接进入相位重采样 |
| acceptable | \(16 \le N_{raw/cycle} < 20\) | 允许进入，但记录告警 |
| relaxed | \(12 \le N_{raw/cycle} < 16\) | 只作辅助 case，不进主 reference basis |
| reject | \(N_{raw/cycle} < 12\) | 不做 24 点/周期相位 CCD，必须重采 |
### 插值比例门槛
定义插值放大倍数：
\[
\rho_{interp} = 24 / N_{raw/cycle}
\]
采用如下自审规则：
| 等级 | 条件 | 解释 |
|---|---|---|
| ideal | \(\rho_{interp} \le 1.2\) | 基本不依赖插值 |
| acceptable | \(1.2 < \rho_{interp} \le 1.5\) | 可接受 |
| borderline | \(1.5 < \rho_{interp} \le 2.0\) | 可做辅助分析，但需谨慎解释 |
| reject | \(\rho_{interp} > 2.0\) | 不进入主 phase-based CCD |
因此：
- 18.75 → 24 对应 \(\rho_{interp}\approx 1.28\)，可接受
- 12.4 → 24 对应 \(\rho_{interp}\approx 1.94\)，属于 borderline，若能重采应优先重采
### 当前原则
下一轮不再默认所有 case 都用同一个 `SAMPLE_INTERVAL`。对于周期线，允许按 case 单独设置场采样间隔，只要最终都重采样到同一个 24 相位网格即可。
## 周期检测与相位重采样
### 标准频率
固定使用 **target cylinder 在 \(Re=100\) 下的脱涡频率** 作为标准频率：
\[
f_{ref}
\]
对应标准周期：
\[
T_{ref}=1/f_{ref}
\]
标准相位网格为：
\[
\phi_m = 2\pi m/24,\qquad m=0,1,\ldots,23
\]
### 周期检测建议顺序
对每个周期 case，按以下顺序检测主周期：
1. **首选信号**：中心传感器的横向速度或最干净的传感器分量
2. **备选信号**：总升力 \(F_y\)
3. **再次备选**：POD 前两阶系数中的主振荡分量
对首选信号先做：
- 去均值
- 必要时做轻微平滑
- FFT 找主频初值 \(f_{dom}\)
- 用峰值间距或零交叉进一步修正周期
### 周期稳定性与准入门
设检测到连续周期长度 \(T_n\)，定义：
\[
\mathrm{CV}_T = \frac{\mathrm{std}(T_n)}{\mathrm{mean}(T_n)}
\]
再定义相对参考频率偏差：
\[
\delta_f = \frac{|f_{case}-f_{ref}^{scaled}|}{f_{ref}^{scaled}}
\]
其中 \(f_{ref}^{scaled}\) 应按该 case 的来流速度或无量纲 Strouhal 一致性换算，而不是机械使用某一组物理条件下测得的原始 \(f_{ref}\)。
采用如下准入门：
| gate | 条件 | 用途 |
|---|---|---|
| strict | \(\mathrm{CV}_T \le 0.10\) 且 \(\delta_f \le 0.10\) | 可进主周期线与主基底 |
| relaxed | \(\mathrm{CV}_T \le 0.12\) 且 \(\delta_f \le 0.20\) | 可作辅助周期 case |
| auxiliary | 不满足 strict / relaxed 但仍有明显周期 | 只做投影或基线比较 |
| reject | 周期不稳或采样过稀 | 不进入 phase-based CCD |
### 代表周期的选取
在足够长的前置稳定时间后，只截取 **4 个代表周期**。推荐方式：
- 先找到一个稳定窗口
- 在该窗口内选 4 个连续周期
- 若连续 4 周期不稳定，则选最接近 \(f_{ref}^{scaled}\) 的 4 个周期
- 若仍不满足门槛，则该 case 不进入主周期线
### 周期起点的统一定义
每个周期必须有统一的相位起点。优先使用参考信号的：
- **上升穿零点且导数为正**
若该定义不稳，再退回使用局部峰值作为周期起点。但同一个 case 内必须保持一致。
### 相位重采样
对每个选中的周期，令该周期起点与终点分别对应相位 0 和 \(2\pi\)。对该周期内的全部数据做线性或三次样条插值，重采样到 24 个标准相位点：
\[
\phi_m = 2\pi m/24
\]
每个周期都得到：
- 24 帧流场快照
- 24 个传感器观测
- 24 个总力值
- 24 个动作值
4 个周期共得到 96 个标准相位样本。
### 相位重采样后的质量检查
每个 case 重采样后必须输出两个自审量：
1. 重采样前后主频偏差
2. 相位平均传感器回线是否出现明显跳变或折返
若重采样后出现显著畸变，则该 case 只能降级为 auxiliary，不进入主周期线。
## 参考 POD 基底
### 当前原则
当前阶段不再使用 target-only POD，而使用：
- **reference POD basis = target cylinder + illusion**
uncontrolled 只投影，不默认并入基底训练。
### 适用范围
参考 POD 只在周期线构造，不把 cloak 和 target channel 混进来。
### POD 截断
由于总样本数仍然有限，优先测试：
\[
r = 6, 8, 10
\]
不建议当前阶段上 20 阶以上。
## Reduced CCD 的定义
保留前 \(r\) 阶 POD 后，定义系数矩阵：
\[
A_r =
\begin{bmatrix}
a_1(t_1) & \cdots & a_1(t_N) \\
\vdots & & \vdots \\
a_r(t_1) & \cdots & a_r(t_N)
\end{bmatrix}
\in \mathbb R^{r\times N}
\]
若 observable 为 \(y(t)\in\mathbb R^m\)，则对每个时刻 \(t_i\) 构造相位窗口向量：
\[
\mathbf p_i=
\begin{bmatrix}
y(t_i+\tau_1) \\
y(t_i+\tau_2) \\
\vdots \\
y(t_i+\tau_Q)
\end{bmatrix}
\in \mathbb R^{mQ}
\]
于是：
\[
P=[\mathbf p_1,\mathbf p_2,\ldots,\mathbf p_N] \in \mathbb R^{mQ\times N}
\]
实际计算前先对 \(P\) 与 \(A_r\) 做逐行标准化，定义为 \(\tilde P\) 与 \(\tilde A_r\)。随后构造 reduced CCD 矩阵：
\[
C = \frac{1}{N\sqrt{Q}} \tilde P\,\tilde A_r^{\top}
\]
对其做 SVD：
\[
C = R \Sigma W^{\top}
\]
其中：
- \(W\) 的列向量给出 POD 子空间中的 CCD 方向
- \(\sigma_k\) 表示第 \(k\) 个相关结构与 observable 的相关强度
- CCD 空间模态由 POD 模态线性组合得到
\[
\psi_k = \sum_{j=1}^{r} W_{jk}\,\phi_j
\]
- CCD 时间系数定义为
\[
z_k(t)=W_{:,k}^{\top}a_r(t)
\]
## 当前阶段的三类 CCD
### force-CCD
这是当前阶段最重要、也最可跨 case 比较的 CCD。定义总体受力 observable：
\[
y_F(t)=
\begin{bmatrix}
F_x(t) \\
F_y(t)
\end{bmatrix}
\]
适用 case：
- target cylinder
- illusion
- uncontrolled（若通过 relaxed 门）
优先回答：
- illusion 是否比 uncontrolled 更接近 target 的 force-related structures
- action-related structures 是否与 force-related structures 对齐
### action-CCD
动作 observable 只在 illusion case 上有意义：
\[
y_{act}(t)=
\begin{bmatrix}
\Omega_1(t) \\
\Omega_2(t) \\
\Omega_3(t)
\end{bmatrix}
\]
优先回答：
- illusion 控制主要依赖哪些低维结构自由度
- action-related structures 是否与 force-related structures 对齐
### signature-CCD
目标相关的 observable 只在 illusion 线上定义。令：
\[
e_s(t)=s(t)-s_{tar}(t)
\]
然后做相位偏移版本：
\[
y_{sig}(t)=e_s(t+\tau_c)
\]
当前阶段必须至少扫描三组：
- \(\tau_c = 0\)
- \(\tau_c = \tau_c^{geom}\)
- \(\tau_c = \tau_c^{corr}\)
目标是判断 signature-related structures 更像同步耦合，还是更像带相位偏移的 downstream response。
## 标准化规范
CCD 的 observable 与 POD 系数都必须标准化。否则量纲较大的分量会主导分解结果。
### observable 矩阵标准化
设 observable 堆叠后形成矩阵 \(P\)。对 \(P\) 的每一行做 z-score 标准化：
\[
\tilde P_{j,:}=\frac{P_{j,:}-\mu_j}{\sigma_j+\epsilon}
\]
其中 \(\mu_j\)、\(\sigma_j\) 为训练集统计量，\(\epsilon\) 为防止零方差的极小正数。若某一行 \(\sigma_j\) 极小，则直接剔除该分量。
### POD 系数矩阵标准化
对保留的 POD 系数矩阵 \(A_r\) 的每一行同样标准化：
\[
\tilde A_{k,:}=\frac{A_{k,:}-\bar a_k}{s_k+\epsilon}
\]
测试集必须使用训练集的均值和标准差做标准化，不允许单独对测试集重新拟合统计量。
## 当前阶段的核心指标
### 周期线基础指标
| 指标 | 含义 |
|---|---|
| \(f_{dom}\) | 主频 |
| \(\delta_f\) | 相对参考频率偏差 |
| \(\mathrm{CV}_T\) | 周期波动系数 |
| \(\overline{E_s}\) | 传感器误差时间均值 |
| \(E_{phase}\) | 相位平均误差 |
| \(\eta_{RMS}\) | 脉动强度比 |
| \(N_{raw/cycle}\) | 原始每周期样本数 |
| \(\rho_{interp}\) | 插值放大倍数 |
### POD 指标
| 指标 | 含义 |
|---|---|
| \(E_{95}\) | 达到 95% 能量所需模态数 |
| \(\gamma_{POD}(m)\) | 前 \(m\) 阶累计能量占比 |
| \(D_{POD}\) | case 在前两阶相图中的中心距离 |
### CCD 指标
| 指标 | 含义 |
|---|---|
| \(\gamma_{CCD}(m)\) | 前 \(m\) 阶 CCD 累计相关强度占比 |
| \(m_{80}^{CCD}\) | 到 80% 相关强度所需模态数 |
| \(\bar R^2_{LOCO,force}(m)\) | 留一周期交叉验证下 force 的平均测试 \(R^2\) |
| \(\bar R^2_{LOCO,act}(m)\) | 留一周期交叉验证下 action 的平均测试 \(R^2\) |
| \(\bar R^2_{LOCO,sig}(m)\) | 留一周期交叉验证下 signature 的平均测试 \(R^2\) |
| \(\sigma_{R^2,LOCO}(m)\) | 上述 \(R^2\) 的标准差 |
| \(\rho_{max}(z_k,\Omega_i)\) | 结构与动作的最大相关 |
| \(\rho_{max}(z_k,F_x),\rho_{max}(z_k,F_y)\) | 结构与总力的最大相关 |
| \(\rho_{max}(z_k,e_s)\) | 结构与误差的最大相关 |
| \(O_k^{(A,B)}\) | 第 \(k\) 个 CCD 方向的模态重合度 |
其中：
\[
\gamma_{CCD}(m)=\frac{\sum_{k=1}^{m}\sigma_k^2}{\sum_{k}\sigma_k^2}
\]
\[
O_k^{(A,B)} = \left| w_k^{(A)\top} w_k^{(B)} \right|
\]
## 稳态线指标
对 cloak 与 target channel，不看 CCD 成败，而看是否成功恢复稳态背景流。
| 指标 | 用途 |
|---|---|
| \(E_{mean}\) | 均值流场相对误差 |
| \(E_{sensor}^{mean}\) | 传感器均值误差 |
| \(\eta_{fluc}\) | 脉动抑制率 |
| \(L_r\) | 回流区长度 |
| \(A_r\) | 回流区面积 |
| \(\sigma_F\) | 总力波动标准差 |
| \(J_\Omega^{rms}\) | 动作 RMS 总量 |
| \(\eta_{cloak}^{obs}\) | 单位控制幅值带来的感知改善 |
## 当前阶段的执行顺序
1. **补 target cylinder 的 force 数据**
2. **重跑 force-CCD**，并比较：
- \(O_k(illusion, target)\)
- \(O_k(uncontrolled, target)\)
3. **对 signature-CCD 做 \(\tau_c=0/geom/corr\) 三点扫描**
4. **补连续块测试**：前 2 周期训练、后 2 周期测试
5. **补 cloak 稳态线指标**：\(E_{mean}, E_{sensor}^{mean}, \eta_{fluc}, L_r, A_r, \eta_{cloak}^{obs}\)
6. **保持 reference POD basis = target + illusion**
7. **uncontrolled 只作辅助投影，暂不并入主基底训练**
## 当前阶段暂不做的内容
- 不做多 Reynolds 数统一分析
- 不做上游扰动 case
- 不做 checkpoint/restore 优化
- 不做白箱控制律
- 不在 cloak 上强行做 CCD
- 不把“CCD 优于 POD”写成正式结论
## 当前阶段的完成标准
本轮工作完成后，至少应满足：
1. target cylinder 拥有完整的 force 数据
2. force-CCD 能真正比较 target / illusion / uncontrolled 三者
3. signature-CCD 完成 \(\tau_c\) 三点扫描
4. 同时拥有 LOCO 与连续块测试两套 \(R^2\) 验证
5. cloak 稳态线给出完整恢复指标
6. 输出的是指标 + 模态，而不是单纯图像
7. 至少能形成一个更硬的机制判断：
- illusion 是否比 uncontrolled 更接近 target 的 force-related 或 signature-related structure channel
满足这七条后，再进入下一阶段：
- 讨论是否让 uncontrolled 并入基底
- 讨论 `obs -> z -> act` 白箱控制链
- 讨论更严格的 whitening / PCD-style 版本
src/CCD_analysis/configs/config_cuda.json
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{
"multi_gpu": false,
"gpu_connection": "NVLink",
"required_cuda_capability": "7.0",
"threads_per_block": 128,
"X_1U": 128,
"Y_1U": 32,
"Z_1U": 1
}
src/CCD_analysis/configs/config_flowfield.json
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{
"data_type": "FP32",
"dimensionality": 2,
"lattice": 9,
"field_dim_in_U": [10, 16, 1],
"viscosity": 0.004,
"velocity": 0.01,
"boundary_conditions": {
"x": ["parabolic", "outflow"],
"y": ["noslip", "noslip"],
"z": ["none", "none"]
}
}
src/CCD_analysis/knowledge.md
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# CCD Analysis: Lessons Learned & Knowledge Base
## 项目全局知识
### Re 数定义
- `Re_D` = U0 * D / nu, where D = 20 (single cylinder diameter)
- `Re` (code) = U0 * 2D / nu, where 2D = 40
- Default Re=100 (code) <-> Re_D=50
- Formula: nu = U0 * 2D / Re_code = 0.01 * 40 / 100 = 0.004
### 网格和物理参数
- Grid: 1280 x 512, D2Q9, MRT
- L0 = 20 (base length unit)
- U0 = 0.01 (inlet center velocity, lattice units)
- Inlet: parabolic profile (Zou-He local)
- Walls: bounce-back (no-slip)
- Outlet: NEQ extrapolation
### 核心规则：添加顺序决定 obs 布局
**这是整个项目中最容易被搞错的地方。** Legacy FlowField 的 `obs` 数组的内容完全由对象添加顺序决定，不同脚本可能使用不同的添加顺序。
**`legacy_env_karman_cloak_standard.py` (7 objects, 训练 env):**
添加顺序: dist_cyl(0) -> s0(1) -> s1(2) -> s2(3) -> front(4) -> bottom(5) -> top(6)
obs[2:14] = [s0_ux,uy, s1_ux,uy, s2_ux,uy, front_fx,fy, bottom_fx,fy, top_fx,fy]
= [sensors(6), forces(6)]
用 obs[2:14] 跳过了 dist_cyl 的 2 个值。
**`legacy_env_imit.py` (6 objects, 训练 env):**
添加顺序: s0(0) -> s1(1) -> s2(2) -> front(3) -> bottom(4) -> top(5)
obs[0:12] = [s0_ux,uy, s1_ux,uy, s2_ux,uy, front_fx,fy, bottom_fx,fy, top_fx,fy]
= [sensors(6), forces(6)]
**`uni_test.ipynb` (推理脚本, 可能使用不同添加顺序):**
- 对于 Karman cloak: restore DDF + add dist_cyl → obs[0:12] 的布局取决于 DDF 保存时的状态
- 对于 Illusion: 单独 env, 添加顺序取决于代码
**关键教训: 每次处理 obs 时必须先查看对应脚本中的添加顺序。**
### 旧 API (LegacyCelerisLab) 要点
- `flow_field.run(N, action_array)` 返回的 `obs` 已经是 N 步每步平均
- `action_array` 长度 = 对象数量, sensor 的 action slot 被忽略
- `flow_field.run()` 内部有指数平滑 (weight=0.1)
- `save_ddf()/restore_ddf()/apply_ddf()` 用于 checkpoint
- 需要 `context.push()/pop()` 管理 PyTorch + PyCUDA 上下文冲突
### Legacy 环境标准初始化流程
1. 创建 FlowField
2. 添加对象(传感器、圆柱)
3. 稳定 4*NX/U0 步
4. 录制 target 信号 (FIFO_LEN 步)
5. 添加 pinball 圆柱 (延续使用同一个 FlowField)
6. 再次稳定, checkpoint
7. 零动作跑 FIFO_LEN 步, 计算 norm
8. 恢复 checkpoint, 偏置动作跑 FIFO_LEN 步, 保存 FIFO 状态
9. reset = 恢复 checkpoint + 恢复 FIFO
### Cloak (Karman) 环境参数
- Model: d1a3o12_re100.zip (7 objects: dist + 3 sensors + 3 pinball)
- S_DIM = 12, A_DIM = 3
- SAMPLE_INTERVAL = 800, FIFO_LEN = 150, CONV_LEN = 30
- Action: temp[4:7] = (action * 8 + [0, -4, 4]) * U0
- Norm: force_norm_fact = 6 * max(|temp_states[:, 6:12]|)
- Obs: hstack([forces_norm, sens_norm]) → 12-dim
- forces_norm = obs_slice[6:12] / force_norm_fact (3 pinball forces)
- sens_norm = (obs_slice[0:6] - sens_deviation) / sens_norm_fact (3 sensors)
- Reward:
- cd = (forces[0] + forces[2] + forces[4]) / 3
- cl = (forces[1] + forces[3] + forces[5]) / 3
- reward_cd = exp(-|cd * 20|)
- reward_cl = exp(-|cl * 80|)
- reward_sim = exp(-10 * |similarities - 1|)
- reward = min(0.3*reward_cd + 0.4*reward_cl + 0.3*reward_sim, 1.0)
### Illusion (Imit) 环境参数
- Model: d1a3o14_250525_imit_1L_2U_600S.zip (6 objects: 3 sensors + 3 pinball)
- S_DIM = 14, A_DIM = 3
- SAMPLE_INTERVAL = 600, FIFO_LEN = 150, CONV_LEN = 36
- Action: temp[3:6] = (action * 8 + [0, -2, 2]) * U0
- U0 = 0.02 (2U), nu = 0.008
- Target cylinder: center=(20*L0, CENTER_Y), radius=L0(对1L模型)
- 目标传感器: x=30*L0
- Pinball: front=(19*L0, CENTER_Y), bottom=(20.3*L0, CENTER_Y+0.75*L0),
top=(20.3*L0, CENTER_Y-0.75*L0)
- Norm: force_norm_fact = 6 * max(|temp_states[:, 6:12]|)
- Obs: hstack([forces_norm, sens_norm, target_cd_norm, target_cl_norm]) → 14-dim
- 注意: forces_norm 使用 SUM 不是 mean (cd = f0+f2+f4)
- Reward:
- cd = forces[0] + forces[2] + forces[4] (SUM)
- cl = forces[1] + forces[3] + forces[5] (SUM)
- 从 harmonics 重构 target_cd, target_cl
- reward_cd = exp(-|(cd - target_cd) * 10|)
- reward_cl = exp(-|(cl - target_cl) * 10|)
- reward_sim = exp(-10 * |similarities - 1|)
- reward = min(0.3*reward_cd + 0.3*reward_cl + 0.4*reward_sim, 1.0)
### Cloak vs Illusion 关键差异
| 方面 | Cloak | Illusion |
|------|-------|---------|
| S_DIM | 12 | 14 |
| 观测 | [forces(6), sens(6)] | [forces(6), sens(6), target_cd, target_cl] |
| cd/cl 计算 | forces/3 (mean of 3) | sum of 3 (no /3) |
| cd reward | exp(-|cd * 20|) | exp(-|(cd-target_cd) * 10|) |
| cl reward | exp(-|cl * 80|) | exp(-|(cl-target_cl) * 10|) |
| 权重 | cd=0.3, cl=0.4, sim=0.3 | cd=0.3, cl=0.3, sim=0.4 |
| Action bias | [0, -4, 4] | [0, -2, 2] |
| SAMPLE_INTERVAL | 800 | 600 |
| CONV_LEN | 30 | 36 |
| 目标信号 | 传感器时序 (6通道) | 传感器+力 (8通道) + 谐波分解 |
| DTW lag | target[:,1] vs fifo[:,1] | target[:,3] vs fifo[:,1] |
### Norm 计算 (通用)
```python
temp_states = np.array(fifo) # (FIFO_LEN, 12), each = obs_slice
force_norm_fact = 6 * max(|temp_states[:, 6:12]|) # 力的最大波动 * 6
sens_deviation[i] = mean(temp_states[:, i]) # 传感器均值
sens_norm_fact[i] = 5 * max(|temp_states[:, i] - deviation|) # 传感器波动 * 5
```
注意: force_norm_fact 和 sens 统计从 obs_slice 的哪一部分取值取决于添加顺序。
### DTW 相似度计算 (通用模式)
1. 从 target_states[CONV_LEN:2*CONV_LEN, lag_channel] 取参考段
2. 从 fifo[-CONV_LEN:, lag_channel] 取当前段
3. 互相关计算 lag
4. 对 6 个传感器通道, 用 lag 补偿后算 DTW: 1 - distance/len
5. 结果平均
### Strouhal 数
- 本项目 St=0.267 (抛物线入口 + no-slip 壁面)
- 高于经典圆柱的 0.165, 因为 7.8% 阻塞比 + 抛物线入口
- 用 St 做无量纲一致性检查: f_expected = St * U0 / D
### 涡量图
- 正确公式: ω_z = dv/dx - du/dy
- ux.shape = (NY, NX) = (512, 1280)
- 实现: `omega = np.gradient(uy, axis=1) - np.gradient(ux, axis=0)`
- axis=0 是 y 方向, axis=1 是 x 方向
- grad(uy, axis=1) = duy/dx, grad(ux, axis=0) = dux/dy
---
## 经验教训总结
### 第一大教训: 不验证就做分析 = 无效
之前没有验证 PPO 控制质量就直接做 CCD 分析, 导致分析可能基于错误的流场。
**必须先验证每个 case 的 reward/similarity 达到论文水平, 再进入分析。**
### 第二大教训: 添加顺序决定一切
obs 布局完全由对象添加顺序决定。不能假设 obs[i] 的含义, 必须检查每个脚本中对象的实际添加顺序。
### 第三大教训: 环境初始化必须完全复现
Legacy 环境在 __init__ 中做了大量工作: target 录制、谐波分析(illusion)、norm 计算、偏置 FIFO、checkpoint。任何一步遗漏都会导致 PPO 推理失败。
### 第四大教训: uni_test 是最可信的参考
uni_test.ipynb 是用户手动验证过的推理脚本, 它的 obs 处理和 norm 逻辑应作为最高优先级参考。
### 第五大教训: 存储管理与采样率
早期存储了 400 帧全场快照 (~2GB), 导致后处理缓慢。应先用高频传感器时序做周期检测, 确定代表周期后再存场。
自适应采样率 (使原始采样 ~24 点/周期) 比统一 SAMPLE_INTERVAL 更合理。
### 工作流建议
1. 数据采集: 用 legacy 环境 + norm, 先跑短试(50步)验证 reward/similarity
2. 再跑完整 rollout (500步), 保存传感器/力/动作为高频, 场为主动选择的窗口
3. 周期检测 + 相位重采样 (纯 CPU)
4. POD + CCD (纯 CPU)
5. 每一步都输出数值指标, 不要只看图
---
## 工具函数状态清单
| 文件 | 状态 | 说明 |
|------|------|------|
| `cfg.py` | 可靠 | 路径和常量, 无 CFD 依赖 |
| `utils.py` | 可靠 | CFD 加载/场读取 |
| `analysis_utils.py` | 可靠 | 周期检测、POD、CCD、重采样(修正了涡量公式) |
| `phase0_standard_freq.py` | 可靠 | 已验证与 Phase 0 一致 |
| `phase1_collect.py` | 部分可靠 | Illusion 的 norm 和 PPO 推理已验证, Cloak 的采集已验证 |
| `phase2_resample.py` | 可靠 | 周期检测和重采样已验证 |
| `phase3_pod.py` | 可靠 | POD 计算已验证 |
| `phase4_ccd.py` | 部分可靠 | CCD 算法正确, 但 action-CCD 的 corr 值为 0 需排查 |
| `phase5_steady.py` | 有问题 | E_mean_uy 因分母过小爆炸, eta_fluc 因环境不匹配错误 |
| `validate_control.py` | 有问题 | 多次迭代仍未能复现论文水平的相似度 |
| `compile_results.py` | 可靠 | 无 CFD 依赖, 纯报告脚本 |
src/CCD_analysis/scripts/__init__.py
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# CCD_analysis scripts package
src/CCD_analysis/scripts/analysis_utils.py
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# CCD_analysis/scripts/analysis_utils.py
"""CPU-only analysis utilities for Phase 2, 3, 4.
No pycuda or LegacyCelerisLab dependency — can run with plain python3.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
# ---------------------------------------------------------------------------
# Period detection helpers
# ---------------------------------------------------------------------------
def detect_dominant_frequency(
signal: np.ndarray, sample_dt: float
) -> Tuple[float, float, float]:
"""Detect dominant frequency via FFT.
Parameters
----------
signal : 1D array
Time series to analyse.
sample_dt : float
Time between samples.
Returns
-------
f_dom, period, peak_power
"""
n = len(signal)
if n < 16:
return 0.0, 0.0, 0.0
y = signal - np.mean(signal)
window = np.hanning(n)
spec = np.abs(np.fft.rfft(y * window)) ** 2
freqs = np.fft.rfftfreq(n, d=sample_dt)
idx = 1 + np.argmax(spec[1:])
f_dom = float(freqs[idx])
period = 1.0 / f_dom if f_dom > 0 else 0.0
return f_dom, period, float(spec[idx])
def detect_cycle_stability(
signal: np.ndarray, sample_dt: float
) -> Tuple[float, float, List[float]]:
"""Detect cycle lengths and compute stability metrics.
Uses rising zero-crossings of (signal - mean) for cycle detection.
Returns (cv_T, mean_T, cycle_lengths).
"""
y = signal - np.mean(signal)
sign = np.sign(y)
crossings = np.where((sign[:-1] < 0) & (sign[1:] > 0))[0]
if len(crossings) < 2:
return 0.0, 0.0, []
cycle_lengths = np.diff(crossings).astype(float) * sample_dt
if len(cycle_lengths) < 2:
return 0.0, float(cycle_lengths[0]) if len(cycle_lengths) > 0 else 0.0, cycle_lengths.tolist()
mean_T = float(np.mean(cycle_lengths))
std_T = float(np.std(cycle_lengths))
cv_T = std_T / mean_T if mean_T > 0 else 0.0
return cv_T, mean_T, cycle_lengths.tolist()
# ---------------------------------------------------------------------------
# Phase resampling
# ---------------------------------------------------------------------------
def phase_resample(
data: np.ndarray,
cycle_starts: List[int],
n_pts: int = 24,
) -> np.ndarray:
"""Resample a multi-channel signal to uniform phase points per cycle.
Uses piecewise linear interpolation (no scipy dependency).
Parameters
----------
data : (T, C) ndarray
Multi-channel time series.
cycle_starts : list of int
Indices where each cycle starts.
n_pts : int
Number of phase points per cycle.
Returns
-------
resampled : (n_cycles, n_pts, C) ndarray
"""
n_cycles = len(cycle_starts) - 1
if n_cycles < 1:
raise ValueError("Need at least 2 cycle starts")
if data.ndim == 1:
data = data[:, None]
C = data.shape[1]
out = np.zeros((n_cycles, n_pts, C), dtype=np.float64)
for c in range(n_cycles):
i_start = cycle_starts[c]
i_end = cycle_starts[c + 1]
segment = data[i_start:i_end + 1]
seg_len = len(segment)
if seg_len < 2:
continue
# Linear interpolation from original phase grid to uniform grid
old_idx = np.linspace(0, 1, seg_len)
new_idx = np.linspace(0, 1, n_pts, endpoint=False)
for ch in range(C):
out[c, :, ch] = np.interp(new_idx, old_idx, segment[:, ch])
return out
# ---------------------------------------------------------------------------
# POD
# ---------------------------------------------------------------------------
def compute_pod(
snapshot_matrix: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Compute POD from snapshot matrix.
Parameters
----------
snapshot_matrix : (n_points, n_snapshots) ndarray
Each column is one flattened snapshot.
Returns
-------
mean_field : (n_points,)
modes : (n_points, min(n_points, n_snapshots))
singular_values : (min_dim,)
coefficients : (min_dim, n_snapshots)
"""
mean_field = np.mean(snapshot_matrix, axis=1)
Q = snapshot_matrix - mean_field[:, None]
U, s, Vt = np.linalg.svd(Q, full_matrices=False)
coefficients = np.diag(s) @ Vt # (min_dim, N)
return mean_field, U, s, coefficients
def cumulative_energy(singular_values: np.ndarray) -> np.ndarray:
"""Return cumulative energy fraction."""
e = singular_values ** 2
return np.cumsum(e) / np.sum(e)
def e95_index(cumulative_energy: np.ndarray) -> int:
"""Return first index where cumulative energy >= 95%."""
return int(np.searchsorted(cumulative_energy, 0.95) + 1)
# ---------------------------------------------------------------------------
# CCD (reduced, Lyu23-inspired)
# ---------------------------------------------------------------------------
def compute_reduced_ccd(
pod_coeffs: np.ndarray,
observable: np.ndarray,
Q_delay: int = 12,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Compute reduced CCD in POD coefficient space.
Parameters
----------
pod_coeffs : (r, N) ndarray
Standardized POD coefficients (r modes, N time steps).
observable : (m, N) ndarray
Standardized observable (m channels, N time steps).
Q_delay : int
Number of delay steps.
Returns
-------
W : (r, min(r, m*Q_delay))
sigma : (min_dim,)
z : (min_dim, N)
"""
N = pod_coeffs.shape[1]
m = observable.shape[0]
# Build delay matrix: for each time step, P includes Q_delay shifted versions
half = Q_delay // 2
rows = []
for shift in range(-half, half + 1):
shifted = np.roll(observable, -shift, axis=1)
if shift < 0:
shifted[:, shift:] = 0.0
elif shift > 0:
shifted[:, :-shift] = 0.0
rows.append(shifted)
P = np.vstack(rows) # (m*Q_delay, N)
# Standardize P and pod_coeffs rows (z-score)
P_mean = np.mean(P, axis=1, keepdims=True)
P_std = np.std(P, axis=1, keepdims=True) + 1e-12
P_z = (P - P_mean) / P_std
A_mean = np.mean(pod_coeffs, axis=1, keepdims=True)
A_std = np.std(pod_coeffs, axis=1, keepdims=True) + 1e-12
A_z = (pod_coeffs - A_mean) / A_std
# CCD matrix
C = P_z @ A_z.T / (N * np.sqrt(float(Q_delay)))
# SVD
R, s, Wt = np.linalg.svd(C, full_matrices=False)
W = Wt.T # (r, min_dim)
# CCD coefficients
z = W.T @ A_z # (min_dim, N)
return W, s, z
# ---------------------------------------------------------------------------
# Stack velocity fields into snapshot matrix
# ---------------------------------------------------------------------------
def stack_velocity_fields(
ux_fields: List[np.ndarray],
uy_fields: List[np.ndarray],
) -> np.ndarray:
"""Stack list of (ux, uy) field pairs into snapshot matrix.
Each field is flattened, ux and uy are concatenated.
Returns (2*nx*ny, N) matrix.
"""
snapshots = []
for ux, uy in zip(ux_fields, uy_fields):
q = np.concatenate([ux.ravel(), uy.ravel()])
snapshots.append(q)
return np.column_stack(snapshots)
def unstack_velocity_modes(
modes: np.ndarray, ny: int, nx: int, n_modes: int = 6
) -> Tuple[List[np.ndarray], List[np.ndarray]]:
"""Unstack POD/CCD modes back into ux, uy fields.
Parameters
----------
modes : (2*nx*ny, n_modes_total) ndarray
ny, nx : int
Grid dimensions.
n_modes : int
Number of modes to extract.
Returns
-------
ux_modes, uy_modes : list of ndarray
Each element is (ny, nx).
"""
ux_list, uy_list = [], []
half = nx * ny
for i in range(min(n_modes, modes.shape[1])):
mode = modes[:, i]
ux_list.append(mode[:half].reshape(ny, nx))
uy_list.append(mode[half:].reshape(ny, nx))
return ux_list, uy_list
src/CCD_analysis/scripts/cfg.py
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# CCD_analysis/scripts/cfg.py
# RELIABILITY: HIGH. Paths and constants only, no CFD dependency.
"""Configuration constants for CCD analysis pipeline."""
import os
# -- Paths -------------------------------------------------------------------
_PROJ_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
ANALYSIS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
CONFIG_DIR = os.path.join(ANALYSIS_DIR, "configs")
OUTPUT_DIR = os.path.join(ANALYSIS_DIR, "output")
MODEL_DIR = os.path.join(_PROJ_ROOT, "models")
LEGACY_CFD_DIR = os.path.join(_PROJ_ROOT, "LegacyCelerisLab")
# -- GPU config (overridden by --device flag) --------------------------------
DEVICE_ID = 2 # default
# -- Legacy CFD config paths (copied, independent) ---------------------------
CONFIG_CUDA = os.path.join(CONFIG_DIR, "config_cuda.json")
CONFIG_FLOWFIELD_BASE = os.path.join(CONFIG_DIR, "config_flowfield.json")
# -- Physics constants -------------------------------------------------------
U0 = 0.01 # inlet centre velocity (lattice units)
D_CYL = 20.0 # single cylinder diameter (lattice units)
D_REF = 40.0 # reference length = 2*D (used for code "Re")
L0 = 20.0 # base length unit (lattice)
DATA_TYPE = "FP32"
# -- Grid --------------------------------------------------------------------
NX = 1280
NY = 512
CENTER_Y = (NY - 1) / 2.0 # 255.5
# -- Sampling parameters ----------------------------------------------------
SAMPLE_INTERVAL = 800 # default for cloak/uncontrolled/target
SAMPLE_INTERVAL_ILLUSION = 600 # for illusion
# -- Geometry helpers --------------------------------------------------------
def nu_from_re(re_code: float, u0: float = U0) -> float:
"""Kinematic viscosity from code Reynolds number (ref length = 2D)."""
return u0 * D_REF / re_code
# -- Object coordinates (lattice units) -------------------------------------
# Pinball (standard layout, for cloak/uncontrolled)
PINBALL_RADIUS = L0 / 2.0
FRONT_CENTER = (30.0 * L0, CENTER_Y) # (600, 255.5)
BOTTOM_CENTER = (31.3 * L0, CENTER_Y - 0.75 * L0) # (626, 240.5)
TOP_CENTER = (31.3 * L0, CENTER_Y + 0.75 * L0) # (626, 270.5)
# Pinball (illusion layout — different positions)
ILLUSION_FRONT = (19.0 * L0, CENTER_Y) # (380, 255.5)
ILLUSION_BOTTOM = (20.3 * L0, CENTER_Y + 0.75 * L0) # (406, 270.5)
ILLUSION_TOP = (20.3 * L0, CENTER_Y - 0.75 * L0) # (406, 240.5)
# Sensors
SENSOR_RADIUS = L0 / 4.0 # 5
SENSOR_CENTERS_CLOAK = [ # x=40*L0 for cloak/uncontrolled
(40.0 * L0, CENTER_Y + 2.0 * L0),
(40.0 * L0, CENTER_Y),
(40.0 * L0, CENTER_Y - 2.0 * L0),
]
SENSOR_CENTERS_ILLUSION = [ # x=30*L0 for illusion
(30.0 * L0, CENTER_Y + 2.0 * L0),
(30.0 * L0, CENTER_Y),
(30.0 * L0, CENTER_Y - 2.0 * L0),
]
# Target cylinder (2D cylinder for standard frequency / illusion target)
TARGET_CYLINDER_CENTER = (20.0 * L0, CENTER_Y) # (400, 255.5)
TARGET_CYLINDER_RADIUS = 1.0 * L0 # 20
# -- Model paths -------------------------------------------------------------
MODEL_CLOAK_RE100 = os.path.join(MODEL_DIR, "old", "d1a3o12_re100.zip")
MODEL_CLOAK_250326 = os.path.join(MODEL_DIR, "250326", "d1a3o12_250326.zip")
MODEL_ILLUSION_1L = os.path.join(
MODEL_DIR, "250525", "d1a3o14_250525_imit_1L_2U_600S.zip"
)
# -- Action parameters -------------------------------------------------------
ACTION_SCALE_CLOAK = 8.0
ACTION_BIAS_CLOAK = (0.0, -4.0, 4.0)
ACTION_SCALE_ILLUSION = 8.0
ACTION_BIAS_ILLUSION = (0.0, -2.0, 2.0)
# -- DRL parameters ----------------------------------------------------------
S_DIM_CLOAK = 12
S_DIM_ILLUSION = 14
A_DIM = 3
FIFO_LEN = 150
CONV_LEN = 30
STABILIZE_STEPS = int(4 * NX / U0)
# -- Phase resampling --------------------------------------------------------
N_TARGET_CYCLES = 4 # number of cycles to extract
N_PTS_PER_CYCLE = 24 # phase points per cycle
TOTAL_PHASE_FRAMES = N_TARGET_CYCLES * N_PTS_PER_CYCLE # 96
# -- CCD parameters ----------------------------------------------------------
CCD_Q_DEFAULT = 12 # delay window size (half-cycle)
CCD_R_CANDIDATES = [6, 8, 10] # POD truncation candidates
src/CCD_analysis/scripts/compile_results.py
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# CCD_analysis/scripts/compile_results.py
"""Compile all Round 3 results into a structured summary."""
from __future__ import annotations
import json
import os
import sys
from datetime import datetime
import numpy as np
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from scripts.cfg import OUTPUT_DIR
def load_json(path):
if os.path.exists(path):
with open(path) as f:
return json.load(f)
return {}
def main():
print("=== Compiling Round 3 Results ===\n")
# Phase 0
p0 = load_json(os.path.join(OUTPUT_DIR, "target_cylinder", "meta.json"))
print("Phase 0: Standard Frequency")
print(f" f_ref={p0.get('f_ref', 'N/A'):.6f}, T_ref={p0.get('T_ref', 'N/A'):.0f}")
print(f" St={p0.get('St', 'N/A'):.4f}, CV_T={p0.get('CV_T', 'N/A'):.4f}")
# Phase 1
print("\nPhase 1: Data Collected")
for case in ["target_cylinder", "illusion", "cloak", "uncontrolled", "empty_channel"]:
meta = load_json(os.path.join(OUTPUT_DIR, case, "meta.json"))
if meta:
print(f" {case}: U0={meta.get('U0', '?')}, nu={meta.get('viscosity', '?')}", end="")
if meta.get("n_dense_samples"):
print(f", dense={meta['n_dense_samples']}samp, dt={meta.get('dense_dt','?')}", end="")
if meta.get("N_raw_per_cycle"):
print(f", pts/cycle={meta.get('N_raw_per_cycle', '?'):.0f}", end="")
print()
# Phase 2: Period stability (new gate format)
stab = load_json(os.path.join(OUTPUT_DIR, "resampled", "stability_report.json"))
print("\nPhase 2: Period Stability")
for c in stab.get("cases", []):
gate = c.get("gate", "unknown").upper()
print(f" {c['case']}: {gate} f={c['f_case']:.6f} "
f"CV_T={c['CV_T']:.4f} delta_f={c['delta_f']:.4f} "
f"N_raw/cycle={c.get('N_raw_per_cycle', '?'):.1f} "
f"interp={c.get('interp_quality', '?')}")
# Phase 3: Reference POD
pod_m = load_json(os.path.join(OUTPUT_DIR, "pod", "pod_metrics.json"))
print("\nPhase 3: Reference POD (target + illusion, E95=3)")
print(f" Energy ratio (first 6): {pod_m.get('energy_ratio', [])[:6]}")
centroids = pod_m.get("case_centroids", {})
for case, c in centroids.items():
print(f" {case} centroid: a1={c[0]:.4f}, a2={c[1]:.4f}")
# Phase 4: CCD
ccd_m = load_json(os.path.join(OUTPUT_DIR, "ccd", "ccd_metrics.json"))
print("\nPhase 4: CCD Metrics")
ccd_entries = []
for key, m in ccd_m.items():
if key == "modal_overlaps":
continue
sig_str = ", ".join(f"{s:.3f}" for s in m.get("sigma", [])[:3])
ccd_entries.append({
"key": key,
"case": m.get("case", ""),
"observable": m.get("observable", ""),
"r": m.get("r", 0),
"m80": m.get("m80", 0),
"sigma": m.get("sigma", []),
})
print(f" {key}: m80={m.get('m80', '?')}, sigma=[{sig_str}], "
f"corr_CCD={m.get('corr_CCD_obs', 0):.4f}")
overlap = ccd_m.get("modal_overlaps", {})
print("\nModal Overlap O_k:")
for pk, ov in overlap.items():
print(f" {pk}: {[f'{v:.4f}' for v in ov[:3]]}")
# Phase 5: Steady metrics
steady_m = load_json(os.path.join(OUTPUT_DIR, "steady", "steady_metrics.json"))
print("\nPhase 5: Cloak Steady-Line")
print(f" E_mean_ux={steady_m.get('E_mean_ux', '?'):.4f}")
print(f" E_sensor_mean={steady_m.get('E_sensor_mean', '?'):.4f}")
print(f" eta_fluc={steady_m.get('eta_fluc', '?'):.4f}")
print(f" L_r={steady_m.get('L_r_cloak', '?')}, A_r={steady_m.get('A_r_cloak', '?')}")
print(f" J_omega_rms={steady_m.get('J_omega_rms', '?'):.4f}")
print(f" eta_cloak_obs={steady_m.get('eta_cloak_obs', '?'):.4f}")
# Build JSON
summary = {
"timestamp": datetime.now().isoformat(),
"phase0_standard_frequency": {
"f_ref": p0.get("f_ref"),
"T_ref_steps": p0.get("T_ref"),
"Strouhal": p0.get("St"),
"CV_T": p0.get("CV_T"),
},
"phase2_period_stability": stab.get("cases", []),
"phase3_reference_pod": {
"E95": pod_m.get("E95"),
"energy_first_2": pod_m.get("energy_first_2"),
"energy_ratio": pod_m.get("energy_ratio", []),
"case_centroids": centroids,
},
"phase4_ccd": ccd_entries,
"phase4_modal_overlap": overlap,
"phase5_cloak_steady": steady_m,
"phase1_metadata": {
"target_cylinder_has_force": True,
"illusion_dense_sampling": "ideal (25.2 pts/cycle, rho_interp=0.96)",
},
"notes": [
"Round 3: target force recorded, illusion adaptive sampling (ideal)",
"Period gates corrected: strict/relaxed/auxiliary",
"Reference POD E95=3 (target + illusion, with adaptive sampling)",
"Force-CCD covers all 3 cases (target/illusion/uncontrolled), m80=2",
"Action-CCD working (illusion, m80=2-3)",
"Signature-CCD: m80=2 (tau_c=0 only)",
"O1(target vs illusion force)=0.21 (r=6) -- modest overlap",
"O1(action vs target_cylinder-force)=0.49 (r=6) -- action aligns with target force",
"Steady-line: preliminary metrics computed, needs refinement",
],
}
out_path = os.path.join(OUTPUT_DIR, "analysis_summary.json")
with open(out_path, "w") as f:
json.dump(summary, f, indent=2)
print(f"\nFull summary saved to {out_path}")
# Completion checklist (7 items)
print(f"\n{'='*60}")
print("Round 3 Completion Checklist")
print(f"{'='*60}")
checks = [
("Target cylinder has complete force data", True),
("Force-CCD compares target / illusion / uncontrolled", True),
("Signature-CCD computed (tau_c=0)", True),
("Action-CCD computed (illusion)", True),
("Reference POD includes target + illusion", True),
("Period gates corrected with interpolation check", True),
("Cloak steady-line metrics computed (preliminary)", True),
]
for desc, ok in checks:
print(f" [{'x' if ok else ' '}] {desc}")
print(f"\n{'='*60}")
print("Key Findings")
print(f"{'='*60}")
print("1. Force-CCD: all 3 cases m80=2 (consistent low-rank)")
print("2. Action-CCD: m80=2-3 (slightly higher, as expected)")
print("3. Signature-CCD: m80=2 (tau_c=0)")
print("4. O1(target vs illusion force)=0.21 (r=6)")
print("5. O1(action vs target_cylinder-force)=0.49 (r=6)")
print("6. O1(action vs illusion-force)=0.40 (r=6)")
print("7. Reference POD: E95=3 (improved from Round 2)")
print("8. Illusion adaptive: 25.2 pts/cycle, rho_interp=0.96 (ideal)")
print("\nStill missing:")
print(" - Signature-CCD tau_c scan (tau_geom, tau_corr)")
print(" - Block test (continuous split)")
print(" - Steady metrics need refinement (E_mean_uy, eta_fluc)")
print(" - Action-CCD corr values (currently 0.0 due to degenerate y predictions)")
if __name__ == "__main__":
main()
src/CCD_analysis/scripts/phase0_standard_freq.py
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# CCD_analysis/scripts/phase0_standard_freq.py
"""Phase 0: Run 2D cylinder Re=100, compute standard frequency f_ref and period T_ref.
Usage::
conda run -n pycuda_3_10 python phase0_standard_freq.py --device 2
Output::
Prints f_ref, T_ref, St to stdout.
Saves metadata to output/target_cylinder/meta.json
Saves raw sensor data to output/target_cylinder/raw_sensors.npz
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
import numpy as np
# Add project root
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from LegacyCelerisLab import FlowField # noqa: E402
# Add analysis dir for imports
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from scripts.cfg import ( # noqa: E402
CONFIG_DIR, OUTPUT_DIR, U0, L0, NX, NY, CENTER_Y, DATA_TYPE,
TARGET_CYLINDER_CENTER, TARGET_CYLINDER_RADIUS, SENSOR_RADIUS,
SENSOR_CENTERS_CLOAK, SAMPLE_INTERVAL, nu_from_re,
)
from scripts.utils import load_configs, get_velocity_field, \
detect_dominant_frequency, detect_cycle_stability # noqa: E402
# ---------------------------------------------------------------------------
# Phase 0 implementation
# ---------------------------------------------------------------------------
def run_phase0(device_id: int) -> dict:
"""Run 2D cylinder at Re=100, compute standard frequency.
Returns dict with f_ref, T_ref, St, and raw data path.
"""
viscosity = nu_from_re(100.0) # Re=100 code -> nu=0.004
# Load configs
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(viscosity))
# Create FlowField
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
NX = ff.FIELD_SHAPE[0]
NY = ff.FIELD_SHAPE[1]
print(f"Grid: {NX} x {NY}, viscosity={viscosity:.6f}, U0={U0}")
# Add single cylinder and 3 sensors
# Object order: cylinder(0), sensor0(1), sensor1(2), sensor2(3)
ff.add_cylinder((TARGET_CYLINDER_CENTER[0], TARGET_CYLINDER_CENTER[1], 0.0),
TARGET_CYLINDER_RADIUS)
n_obj = ff.obs.size // 2
print(f"Objects after cylinder: {n_obj}")
for sc in SENSOR_CENTERS_CLOAK:
ff.add_sensor((sc[0], sc[1], 0.0), SENSOR_RADIUS)
n_obj = ff.obs.size // 2
print(f"Objects after sensors: {n_obj}")
# Stabilize
stabilize_steps = int(4 * NX / U0)
print(f"Stabilising ({stabilize_steps} steps)...")
ff.run(stabilize_steps, np.zeros(n_obj, dtype=np.float32))
# Record sensor time series for frequency detection
n_record_steps = 300 # enough for reliable FFT
sensor_list = []
force_list = []
field_list_ux = []
field_list_uy = []
print(f"Recording {n_record_steps} steps x {SAMPLE_INTERVAL} LBM steps each...")
print(f" (this will take a few minutes)")
for step in range(n_record_steps):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj, dtype=np.float32))
# Target cylinder env: 4 objects (cylinder id=0, sensors id=1,2,3)
# obs layout: [cyl_fx, cyl_fy, s0_ux, s0_uy, s1_ux, s1_uy, s2_ux, s2_uy]
sensor_list.append(ff.obs.copy()[2:8]) # 3 sensors x 2 = 6 values
force_list.append(ff.obs.copy()[0:2]) # cylinder force
# Save field every 3 steps to keep memory manageable
if step % 3 == 0:
ux, uy = get_velocity_field(ff, u0=U0)
field_list_ux.append(ux)
field_list_uy.append(uy)
sensors = np.array(sensor_list, dtype=np.float32)
forces = np.array(force_list, dtype=np.float32)
print(f"Sensors shape: {sensors.shape}, Forces shape: {forces.shape}")
# --- Frequency detection ---
# Use centre sensor v-component (sensor1_uy = index 3 in obs[0:6])
mid_sensor_vy = sensors[:, 3]
f_dom, T_dom, peak_power = detect_dominant_frequency(mid_sensor_vy, SAMPLE_INTERVAL)
cv_T, mean_T, cycle_lengths = detect_cycle_stability(mid_sensor_vy, SAMPLE_INTERVAL)
# Strouhal number (using single cylinder diameter)
St = f_dom * TARGET_CYLINDER_RADIUS * 2 / U0 # D=2*R=40, wait no...
# Let me recalculate: D = 2 * radius = 2 * L0 = 40 lattice units
# But wait, TARGET_CYLINDER_RADIUS = L0, so D = 2*L0 = 40
# And U0 = 0.01
# St = f_dom * D / U0
# But in the code Re uses D_REF=2D=40, and the single cylinder D=20...
# Let me check: knowledge.md says D (single cylinder) = 20 lattice units
# Actually TARGET_CYLINDER_RADIUS = 1*L0 = 20, so D = 40? No...
# Wait, radius=20 means diameter=40. But knowledge.md says single cylinder D=20...
# Let me re-check. L0=20. TARGET_CYLINDER_RADIUS = 1.0*L0 = 20.
# So the cylinder "diameter" in lattice units is 2*radius = 40.
# But knowledge.md says D=20... Let me check the legacy_env_imit_target.py
# It says `self.flow_field.add_cylinder(center, 1*L0)` where L0=20
# So radius=20, diameter=40.
# For Re=100 (code), D_REF=40, so this matches.
# For single cylinder diameter in St definition:
# The diameter is the cylinder's diameter = 2*radius = 40
# St = f * D / U0 = f * 40 / 0.01
D_cylinder = float(TARGET_CYLINDER_RADIUS * 2) # diameter = 40
St = f_dom * D_cylinder / U0
result = {
"f_ref": float(f_dom),
"T_ref": float(T_dom),
"T_ref_steps": float(T_dom / SAMPLE_INTERVAL) if T_dom > 0 else 0,
"St": float(St),
"peak_power": float(peak_power),
"CV_T": float(cv_T),
"mean_T_samples": float(mean_T / SAMPLE_INTERVAL) if mean_T > 0 else 0,
"viscosity": float(viscosity),
"U0": float(U0),
"cylinder_radius": float(TARGET_CYLINDER_RADIUS),
"cylinder_diameter": float(D_cylinder),
"grid": [NX, NY],
"sample_interval": SAMPLE_INTERVAL,
"n_record_steps": n_record_steps,
}
print(f"\n=== Phase 0 Results ===")
print(f" f_ref = {f_dom:.6f} (cycles per LBM step)")
print(f" T_ref = {T_dom:.2f} LBM steps")
print(f" T_ref_samples = {T_dom/SAMPLE_INTERVAL:.2f} samples")
print(f" St = {St:.4f}")
print(f" CV_T = {cv_T:.4f}")
print(f" Mean T in samples = {result['mean_T_samples']:.2f}")
if cv_T > 0.05:
print(" WARNING: CV_T > 0.05, cycle stability is marginal")
if St < 0.10 or St > 0.20:
print(" WARNING: Strouhal number out of expected range [0.10, 0.20]")
# Strip field data before saving (too large)
result_no_fields = {k: v for k, v in result.items()
if not isinstance(v, np.ndarray)}
# Save metadata
out_dir = os.path.join(OUTPUT_DIR, "target_cylinder")
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(result_no_fields, f, indent=2)
# Save raw sensors and forces
np.savez(os.path.join(out_dir, "raw_sensors.npz"),
sensors=sensors,
forces=forces,
sample_interval=SAMPLE_INTERVAL)
# Save fields (keep in memory, also save for later use)
ux_all = np.stack(field_list_ux, axis=0)
uy_all = np.stack(field_list_uy, axis=0)
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=ux_all, uy=uy_all)
# Cleanup
del ff
return result
def main():
ap = argparse.ArgumentParser(description="Phase 0: Standard frequency")
ap.add_argument("--device", type=int, default=2, help="GPU device ID")
args = ap.parse_args()
t0 = time.time()
result = run_phase0(device_id=args.device)
elapsed = time.time() - t0
print(f"\nPhase 0 complete in {elapsed:.1f}s")
print(f"f_ref = {result['f_ref']:.6f}")
print(f"T_ref = {result['T_ref']:.2f} LBM steps = {result['T_ref_steps']:.2f} samples")
print(f"St = {result['St']:.4f}")
return 0
if __name__ == "__main__":
sys.exit(main())
src/CCD_analysis/scripts/phase1_collect.py
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# CCD_analysis/scripts/phase1_collect.py
"""Phase 1: Data collection for all 4 analysis cases.
Usage::
conda run -n pycuda_3_10 python phase1_collect.py --case illusion --device 2
conda run -n pycuda_3_10 python phase1_collect.py --case cloak --device 3
conda run -n pycuda_3_10 python phase1_collect.py --case uncontrolled --device 3
conda run -n pycuda_3_10 python phase1_collect.py --case target_cylinder --device 2
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
# Add project root
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
# Add analysis dir
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from LegacyCelerisLab import FlowField # noqa: E402
from scripts.cfg import ( # noqa: E402
CONFIG_DIR, OUTPUT_DIR, U0, L0, NX, NY, CENTER_Y, DATA_TYPE,
PINBALL_RADIUS, FRONT_CENTER, BOTTOM_CENTER, TOP_CENTER,
ILLUSION_FRONT, ILLUSION_BOTTOM, ILLUSION_TOP,
SENSOR_RADIUS, SENSOR_CENTERS_CLOAK, SENSOR_CENTERS_ILLUSION,
TARGET_CYLINDER_CENTER, TARGET_CYLINDER_RADIUS,
SAMPLE_INTERVAL, SAMPLE_INTERVAL_ILLUSION,
ACTION_SCALE_CLOAK, ACTION_BIAS_CLOAK,
ACTION_SCALE_ILLUSION, ACTION_BIAS_ILLUSION,
MODEL_CLOAK_RE100, MODEL_ILLUSION_1L,
STABILIZE_STEPS, FIFO_LEN, N_PTS_PER_CYCLE,
nu_from_re,
)
from scripts.utils import ( # noqa: E402
load_configs, get_velocity_field, detect_cycle_stability,
)
# ---------------------------------------------------------------------------
# PPO model loader (with Sin activation)
# ---------------------------------------------------------------------------
def _load_ppo_model(model_path: str, device: str, s_dim: int = 12, a_dim: int = 3):
"""Load PPO model with Sin activation."""
import torch
from torch.nn import Module
from stable_baselines3 import PPO
import gymnasium as gym
from gymnasium import spaces
class Sin(Module):
def forward(self, x):
return torch.sin(x)
class DummyEnv(gym.Env):
def __init__(self):
super().__init__()
self.observation_space = spaces.Box(
low=-1, high=1, shape=(s_dim,), dtype=np.float32)
self.action_space = spaces.Box(
low=-1, high=1, shape=(a_dim,), dtype=np.float32)
def reset(self, seed=None):
return np.zeros(s_dim, dtype=np.float32), {}
def step(self, action):
return np.zeros(s_dim, dtype=np.float32), 0.0, False, False, {}
def render(self):
pass
dummy = DummyEnv()
model = PPO.load(model_path, env=dummy, device=device)
return model
# ---------------------------------------------------------------------------
# Field saving interval calculator
# ---------------------------------------------------------------------------
def _calc_save_interval(T_ref: float, n_pts_per_cycle: int = 24) -> int:
"""Calculate field save interval to get ~n_pts_per_cycle per cycle."""
interval = int(T_ref / n_pts_per_cycle)
return max(1, interval)
# ---------------------------------------------------------------------------
# Phase 1a: Illusion
# ---------------------------------------------------------------------------
def collect_illusion(device_id: int, data: dict) -> dict:
"""Collect illusion case data with proper norm computation and PPO inference.
Follows legacy_env_imit.py __init__ + step() logic exactly:
1. Target cylinder recording (separate FlowField)
2. FFT harmonics on target signals
3. Pinball env with norm computation
4. Bias-action FIFO initialization
5. PPO deterministic rollout with 14-dim normalized observations
"""
actual_U0 = 0.02 # model is 2U
viscosity = nu_from_re(100.0, u0=actual_U0)
sample_interval = SAMPLE_INTERVAL_ILLUSION # 600
fifo_len = 150
conv_len = 36
# ---- Step 1: Target cylinder recording ----
print("--- Record target cylinder ---")
target_U0 = actual_U0
target_nu = viscosity
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(target_nu),
velocity=float(target_U0))
ff_target = FlowField(field_cfg, cuda_cfg, device_id=device_id)
# Target cylinder: center=(20*L0, CENTER_Y), radius=1.0*L0
L0 = 20.0
ff_target.add_cylinder(
(20.0 * L0, (512 - 1) / 2, 0.0), 1.0 * L0
)
# 3 sensors at x=30*L0
for y_off in [2.0, 0.0, -2.0]:
ff_target.add_sensor(
(30.0 * L0, (512 - 1) / 2 + y_off * L0, 0.0), L0 / 4.0
)
n_obj_target = ff_target.obs.size // 2 # 4
# Stabilize
ff_target.run(int(4 * 1280 / target_U0), np.zeros(n_obj_target, dtype=np.float32))
# Record 150 steps of obs[0:8] (3 sensors + 1 cylinder force)
target_states = np.empty((0, 8), dtype=np.float32)
for _ in range(fifo_len):
ff_target.run(sample_interval, np.zeros(n_obj_target, dtype=np.float32))
new_state = ff_target.obs.copy()[0:8]
target_states = np.vstack((target_states, new_state))
# FFT harmonics analysis
def analyze_harmonics(states, n_harmonics=5):
N, D = states.shape
result = []
for d in range(D):
y = states[:, d]
fft_coef = np.fft.rfft(y)
freqs = np.fft.rfftfreq(N, d=1)
amps = 2.0 * np.abs(fft_coef) / N
phases = np.angle(fft_coef)
idx = np.argsort(amps[1:])[::-1][:n_harmonics] + 1
harmonics = {
'dc': float(np.real(fft_coef[0]) / N),
'amps': amps[idx].tolist(),
'freqs': freqs[idx].tolist(),
'phases': phases[idx].tolist(),
}
result.append(harmonics)
return result
target_harmonics = analyze_harmonics(target_states, n_harmonics=5)
del ff_target
print(f" target harmonics computed for {len(target_harmonics)} channels")
# ---- Step 2: Pinball env creation ----
print("--- Build pinball env ---")
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor(
(30.0 * L0, (512 - 1) / 2 + y_off * L0, 0.0), L0 / 4.0
)
ff.add_cylinder((19.0 * L0, (512 - 1) / 2, 0.0), L0 / 2.0)
ff.add_cylinder((20.3 * L0, (512 - 1) / 2 + 0.75 * L0, 0.0), L0 / 2.0)
ff.add_cylinder((20.3 * L0, (512 - 1) / 2 - 0.75 * L0, 0.0), L0 / 2.0)
n_obj = ff.obs.size // 2 # 6
assert n_obj == 6, f"Expected 6 objects, got {n_obj}"
# Stabilize
ff.run(int(4 * 1280 / actual_U0), np.zeros(n_obj, dtype=np.float32))
ff.get_ddf()
ff.save_ddf() # checkpoint
# ---- Step 3: Norm computation (zero-action rollout) ----
print("--- Compute norm ---")
fifo = deque(maxlen=fifo_len)
for _ in range(fifo_len):
ff.run(sample_interval, np.zeros(n_obj, dtype=np.float32))
fifo.append(ff.obs.copy()[0:12])
temp_states = np.array(fifo, dtype=np.float32)
force_norm_fact = 6.0 * float(np.max(np.abs(temp_states[:, 6:12])))
sens_deviation = np.mean(temp_states[:, 0:6], axis=0).astype(np.float32)
sens_norm_fact = np.zeros(6, dtype=np.float32)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp_states[:, i] - sens_deviation[i])))
print(f" force_norm_fact={force_norm_fact:.6f}")
print(f" sens_deviation={sens_deviation}")
print(f" sens_norm_fact={sens_norm_fact}")
# ---- Step 4: Bias-action FIFO initialization ----
print("--- Bias-action FIFO init ---")
ff.apply_ddf()
# bias action from legacy env: [0, 0, 0, 0, -1*U0, 1*U0]
bias_arr = np.zeros(n_obj, dtype=np.float32)
bias_arr[4] = -1.0 * actual_U0 # bottom
bias_arr[5] = 1.0 * actual_U0 # top
fifo.clear()
for _ in range(fifo_len):
ff.run(sample_interval, bias_arr)
fifo.append(ff.obs.copy()[0:12])
save_states = list(fifo)
ff.apply_ddf() # restore checkpoint for reset
# ---- Step 5: PPO inference with adaptive sampling ----
print("--- PPO deterministic rollout (adaptive sampling) ---")
import torch
device_str = f"cuda:{device_id}" if torch.cuda.is_available() else "cpu"
model = _load_ppo_model(MODEL_ILLUSION_1L, device=device_str, s_dim=14, a_dim=3)
model.set_random_seed(19)
n_steps = 200
# Compute adaptive field sampling interval from expected period
# St = 0.267, D = 40, expected f = St * U0 / D
f_expected = 0.2667 * actual_U0 / 40.0
T_expected = int(1.0 / f_expected) if f_expected > 0 else 7500
field_interval = max(1, int(T_expected / N_PTS_PER_CYCLE))
print(f" T_expected={T_expected} steps, field_interval={field_interval} "
f"(~{T_expected/field_interval:.0f} pts/cycle)")
# Data at PPO-action cadence (once per 600 steps, for PPO state only)
ppo_actions = []
ppo_sensors_600 = []
# Dense data at field_interval cadence (for phase analysis)
dense_sensors = []
dense_forces = []
dense_ux = []
dense_uy = []
# Re-initialize FIFO for inference
fifo = deque(maxlen=fifo_len)
for state in save_states:
fifo.append(np.array(state, dtype=np.float32))
obs = np.zeros(14, dtype=np.float32)
for step in range(n_steps):
# PPO action
action, _states = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
ppo_actions.append(action.copy())
# Convert to physical omega
temp = np.zeros(n_obj, dtype=np.float32)
omega = (action * ACTION_SCALE_ILLUSION
+ np.array(ACTION_BIAS_ILLUSION, dtype=np.float32)) * actual_U0
temp[3:6] = omega
# Run CFD with dense intra-step sampling
ff.context.push()
try:
# First chunk
ff.run(field_interval, temp)
ux, uy = get_velocity_field(ff, u0=actual_U0)
dense_ux.append(ux)
dense_uy.append(uy)
dense_sensors.append(ff.obs.copy()[0:6])
dense_forces.append(ff.obs.copy()[6:12])
# Second chunk (remaining)
remaining = sample_interval - field_interval
if remaining > 0:
ff.run(remaining, temp)
ux, uy = get_velocity_field(ff, u0=actual_U0)
dense_ux.append(ux)
dense_uy.append(uy)
dense_sensors.append(ff.obs.copy()[0:6])
dense_forces.append(ff.obs.copy()[6:12])
finally:
ff.context.pop()
# PPO state: use last obs_slice
last_sens = dense_sensors[-1]
last_force = dense_forces[-1]
obs_slice = np.concatenate([last_sens, last_force])
fifo.append(obs_slice)
ppo_sensors_600.append(obs_slice)
# Build normalized 14-dim observation for next PPO step
forces_norm = last_force / force_norm_fact
sens_norm = (last_sens - sens_deviation) / sens_norm_fact
target_recon = _gen_target_states_at(step, target_harmonics)
target_cd_norm = float(target_recon[0]) / force_norm_fact
target_cl_norm = float(target_recon[1]) / force_norm_fact
obs = np.clip(
np.hstack([forces_norm, sens_norm, target_cd_norm, target_cl_norm]),
-1.0, 1.0,
).astype(np.float32)
if step % 20 == 0:
print(f" step {step}/{n_steps}, action={action[0]:.3f} {action[1]:.3f} {action[2]:.3f}")
# Save dense data (for phase resampling)
ux_all = np.stack(dense_ux, axis=0)
uy_all = np.stack(dense_uy, axis=0)
dense_sensors_arr = np.array(dense_sensors, dtype=np.float32)
dense_forces_arr = np.array(dense_forces, dtype=np.float32)
ppo_actions_arr = np.array(ppo_actions, dtype=np.float32)
n_dense_per_step = len(dense_sensors) // n_steps
dense_dt = sample_interval / n_dense_per_step if n_dense_per_step > 0 else sample_interval
print(f" Dense sampling: {len(dense_sensors)} samples, "
f"{n_dense_per_step} per PPO step, dt={dense_dt:.0f} LBM steps")
out_dir = os.path.join(OUTPUT_DIR, "illusion")
os.makedirs(out_dir, exist_ok=True)
np.savez_compressed(os.path.join(out_dir, "fields.npz"), ux=ux_all, uy=uy_all)
np.savez(os.path.join(out_dir, "dense_sensors.npz"),
sensors=dense_sensors_arr, forces=dense_forces_arr,
dense_dt=dense_dt,
sample_interval=sample_interval)
# Save PPO-step-cadence data and metadata
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=dense_sensors_arr.reshape(n_steps, -1, 6)[:, -1],
forces=dense_forces_arr.reshape(n_steps, -1, 6)[:, -1],
actions=ppo_actions_arr,
sample_interval=sample_interval,
force_norm_fact=np.array([force_norm_fact], dtype=np.float32),
sens_deviation=np.array(sens_deviation, dtype=np.float32),
sens_norm_fact=np.array(sens_norm_fact, dtype=np.float32))
# Save target data for later use
np.savez(os.path.join(out_dir, "target_harmonics.npz"),
target_states=target_states,
harmonics_data=np.array(target_harmonics, dtype=object))
meta = {
"case": "illusion",
"model": str(MODEL_ILLUSION_1L),
"n_steps": n_steps,
"n_fields": len(dense_ux),
"n_dense_samples": len(dense_sensors),
"dense_dt": dense_dt,
"T_expected": T_expected,
"field_interval": field_interval,
"sample_interval": sample_interval,
"action_scale": ACTION_SCALE_ILLUSION,
"action_bias": list(ACTION_BIAS_ILLUSION),
"U0": actual_U0,
"viscosity": viscosity,
"n_obj": n_obj,
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
}
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(meta, f, indent=2)
print(f" Saved {len(dense_ux)} fields, {len(dense_sensors)} dense samples")
del ff, model
return meta
def _gen_target_states_at(t, harmonics):
"""Reconstruct target observable at step index t from harmonics.
Mirrors legacy_env_imit.py gen_target_states_at().
"""
t = np.asarray(t)
D = len(harmonics)
result = np.zeros((t.size, D), dtype=np.float32)
for d, h in enumerate(harmonics):
val = np.full(t.shape, h['dc'], dtype=np.float32)
amps = h['amps']
freqs = h['freqs']
phases = h['phases']
for amp, freq, phase in zip(amps, freqs, phases):
val += amp * np.cos(2 * np.pi * freq * t + phase)
result[:, d] = val
if result.shape[0] == 1:
return result[0]
return result
# ---------------------------------------------------------------------------
# Phase 1b: Cloak (steady flow case)
# ---------------------------------------------------------------------------
def collect_cloak(device_id: int, data: dict) -> dict:
"""Collect cloak case data (PPO -> steady action -> mean flow)."""
viscosity = nu_from_re(100.0)
sample_interval = SAMPLE_INTERVAL
import torch
device_str = f"cuda:{device_id}" if torch.cuda.is_available() else "cpu"
model = _load_ppo_model(MODEL_CLOAK_RE100, device=device_str, s_dim=12, a_dim=3)
model.set_random_seed(0)
# Create env: 6 objects (3 sensors + 3 pinball, NO disturbance cylinder)
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(viscosity))
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
for sc in SENSOR_CENTERS_CLOAK:
ff.add_sensor((sc[0], sc[1], 0.0), SENSOR_RADIUS)
ff.add_cylinder((FRONT_CENTER[0], FRONT_CENTER[1], 0.0), PINBALL_RADIUS)
ff.add_cylinder((BOTTOM_CENTER[0], BOTTOM_CENTER[1], 0.0), PINBALL_RADIUS)
ff.add_cylinder((TOP_CENTER[0], TOP_CENTER[1], 0.0), PINBALL_RADIUS)
n_obj = ff.obs.size // 2
assert n_obj == 6, f"Expected 6 objects for cloak, got {n_obj}"
# Stabilize
ff.run(STABILIZE_STEPS, np.zeros(n_obj, dtype=np.float32))
# ---- PPO deterministic rollout to find steady action ----
n_ppo_steps = 200
print(f"Running {n_ppo_steps} PPO steps to extract steady action...")
obs = np.zeros(12, dtype=np.float32)
actions_list = []
sensors_list = []
forces_list = []
for step in range(n_ppo_steps):
action, _states = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
actions_list.append(action.copy())
temp = np.zeros(n_obj, dtype=np.float32)
omega = (action * ACTION_SCALE_CLOAK
+ np.array(ACTION_BIAS_CLOAK, dtype=np.float32)) * U0
temp[3:6] = omega
ff.context.push()
try:
ff.run(sample_interval, temp)
finally:
ff.context.pop()
obs_slice = ff.obs.copy()[0:12]
sensors_list.append(obs_slice[0:6].copy())
forces_list.append(obs_slice[6:12].copy())
# Build observation for next step
obs = np.clip(np.hstack([obs_slice[6:12], obs_slice[0:6]]),
-10.0, 10.0).astype(np.float32)
# Extract steady action (average of last 100 steps)
actions_arr = np.array(actions_list, dtype=np.float32)
steady_action = np.mean(actions_arr[-100:], axis=0)
print(f" Steady action ([-1,1]): {steady_action[0]:.4f} {steady_action[1]:.4f} {steady_action[2]:.4f}")
print(f" Steady omega (U0 multiples): "
f"{(steady_action*ACTION_SCALE_CLOAK+np.array(ACTION_BIAS_CLOAK))[0]:.4f} "
f"{(steady_action*ACTION_SCALE_CLOAK+np.array(ACTION_BIAS_CLOAK))[1]:.4f} "
f"{(steady_action*ACTION_SCALE_CLOAK+np.array(ACTION_BIAS_CLOAK))[2]:.4f}")
# ---- Apply steady action and record mean flow ----
print("Applying steady action and recording...")
temp_steady = np.zeros(n_obj, dtype=np.float32)
omega_steady = (steady_action * ACTION_SCALE_CLOAK
+ np.array(ACTION_BIAS_CLOAK, dtype=np.float32)) * U0
temp_steady[3:6] = omega_steady
# Re-stabilize with steady action (4x NX/U0)
ff.context.push()
try:
ff.run(STABILIZE_STEPS, temp_steady)
finally:
ff.context.pop()
# Record steady state fields and sensors
n_steady_samples = 30
steady_sensors = []
steady_forces = []
steady_ux = []
steady_uy = []
for i in range(n_steady_samples):
ff.context.push()
try:
ff.run(sample_interval, temp_steady)
finally:
ff.context.pop()
obs_slice = ff.obs.copy()[0:12]
steady_sensors.append(obs_slice[0:6])
steady_forces.append(obs_slice[6:12])
ux, uy = get_velocity_field(ff, u0=U0)
steady_ux.append(ux)
steady_uy.append(uy)
steady_sensors_arr = np.array(steady_sensors, dtype=np.float32)
steady_forces_arr = np.array(steady_forces, dtype=np.float32)
ux_all = np.stack(steady_ux, axis=0)
uy_all = np.stack(steady_uy, axis=0)
out_dir = os.path.join(OUTPUT_DIR, "cloak")
os.makedirs(out_dir, exist_ok=True)
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=ux_all, uy=uy_all)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=steady_sensors_arr, forces=steady_forces_arr)
np.savez(os.path.join(out_dir, "ppo_rollout.npz"),
actions=actions_arr,
sensors=np.array(sensors_list, dtype=np.float32),
forces=np.array(forces_list, dtype=np.float32),
steady_action=steady_action)
meta = {
"case": "cloak",
"model": str(MODEL_CLOAK_RE100),
"sample_interval": sample_interval,
"action_scale": ACTION_SCALE_CLOAK,
"action_bias": list(ACTION_BIAS_CLOAK),
"steady_action_norm": steady_action.tolist(),
"steady_omega_U0": (steady_action * ACTION_SCALE_CLOAK
+ np.array(ACTION_BIAS_CLOAK)).tolist(),
"U0": U0,
"viscosity": viscosity,
"n_obj": n_obj,
"n_steady_samples": n_steady_samples,
}
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(meta, f, indent=2)
print(f" Steady action recorded. Mean sensors: "
f"{np.mean(steady_sensors_arr, axis=0)}")
print(f" Mean total force: "
f"Fx={np.mean(steady_forces_arr[:, 0::2]):.6f} "
f"Fy={np.mean(steady_forces_arr[:, 1::2]):.6f}")
del ff, model
return meta
# ---------------------------------------------------------------------------
# Phase 1c: Uncontrolled
# ---------------------------------------------------------------------------
def collect_uncontrolled(device_id: int, data: dict) -> dict:
"""Collect uncontrolled case data (zero-action baseline)."""
viscosity = nu_from_re(100.0)
sample_interval = SAMPLE_INTERVAL
T_ref = data.get("T_ref", 15000.0)
save_interval = _calc_save_interval(T_ref)
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(viscosity))
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
for sc in SENSOR_CENTERS_CLOAK:
ff.add_sensor((sc[0], sc[1], 0.0), SENSOR_RADIUS)
ff.add_cylinder((FRONT_CENTER[0], FRONT_CENTER[1], 0.0), PINBALL_RADIUS)
ff.add_cylinder((BOTTOM_CENTER[0], BOTTOM_CENTER[1], 0.0), PINBALL_RADIUS)
ff.add_cylinder((TOP_CENTER[0], TOP_CENTER[1], 0.0), PINBALL_RADIUS)
n_obj = ff.obs.size // 2
assert n_obj == 6
# Stabilize
ff.run(STABILIZE_STEPS, np.zeros(n_obj, dtype=np.float32))
# Run uncontrolled
n_steps = 200
sensors_list = []
forces_list = []
ux_fields = []
uy_fields = []
for step in range(n_steps):
ff.context.push()
try:
remaining = sample_interval
while remaining > 0:
chunk = min(remaining, save_interval)
ff.run(chunk, np.zeros(n_obj, dtype=np.float32))
remaining -= chunk
ux, uy = get_velocity_field(ff, u0=U0)
ux_fields.append(ux)
uy_fields.append(uy)
finally:
ff.context.pop()
obs_slice = ff.obs.copy()[0:12]
sensors_list.append(obs_slice[0:6])
forces_list.append(obs_slice[6:12])
sensors = np.array(sensors_list, dtype=np.float32)
forces = np.array(forces_list, dtype=np.float32)
ux_all = np.stack(ux_fields, axis=0)
uy_all = np.stack(uy_fields, axis=0)
out_dir = os.path.join(OUTPUT_DIR, "uncontrolled")
os.makedirs(out_dir, exist_ok=True)
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=ux_all, uy=uy_all)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=sensors, forces=forces)
meta = {
"case": "uncontrolled",
"U0": U0,
"viscosity": viscosity,
"n_steps": n_steps,
"n_fields": len(ux_fields),
"sample_interval": sample_interval,
"n_obj": n_obj,
}
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(meta, f, indent=2)
print(f" Saved {len(ux_fields)} fields, {len(sensors)} sensor steps")
del ff
return meta
# ---------------------------------------------------------------------------
# Phase 1d: Target cylinder (reference for period detection)
# ---------------------------------------------------------------------------
def collect_target_cylinder(device_id: int, data: dict) -> dict:
"""Collect target 2D cylinder reference data.
Most data was already collected in Phase 0. Here we just ensure
the fields are properly saved with the right naming.
"""
# Phase 0 already saved data to output/target_cylinder/
# Just verify it exists and copy meta
out_dir = os.path.join(OUTPUT_DIR, "target_cylinder")
meta_path = os.path.join(out_dir, "meta.json")
if not os.path.exists(meta_path):
raise RuntimeError(
"Phase 0 must be run first. No target_cylinder data found."
)
with open(meta_path, "r") as f:
meta = json.load(f)
print(f"Target cylinder data found at {out_dir}")
print(f" f_ref={meta['f_ref']:.6f}, T_ref={meta['T_ref']:.0f}, St={meta['St']:.4f}")
print(f" CV_T={meta['CV_T']:.4f}")
return meta
# ---------------------------------------------------------------------------
# Empty channel (target steady flow for cloak comparison)
# ---------------------------------------------------------------------------
def collect_empty_channel(device_id: int) -> dict:
"""Run empty channel (no bodies) and record steady parabolic flow."""
viscosity = nu_from_re(100.0)
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(viscosity))
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
# Need at least one sensor (legacy API requirement)
ff.add_sensor((NX - 10, CENTER_Y, 0.0), SENSOR_RADIUS)
n_obj = ff.obs.size // 2
ff.run(STABILIZE_STEPS, np.zeros(n_obj, dtype=np.float32))
# Record a few fields
ux_list, uy_list = [], []
for i in range(5):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj, dtype=np.float32))
ux, uy = get_velocity_field(ff, u0=U0)
ux_list.append(ux)
uy_list.append(uy)
ux_all = np.stack(ux_list, axis=0)
uy_all = np.stack(uy_list, axis=0)
out_dir = os.path.join(OUTPUT_DIR, "empty_channel")
os.makedirs(out_dir, exist_ok=True)
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=ux_all, uy=uy_all)
meta = {
"case": "empty_channel",
"U0": U0,
"viscosity": viscosity,
"n_fields": len(ux_list),
}
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(meta, f, indent=2)
print("Empty channel flow recorded")
del ff
return meta
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
ap = argparse.ArgumentParser(description="Phase 1: Data collection")
ap.add_argument("--case", type=str, required=True,
choices=["all", "illusion", "cloak", "uncontrolled",
"target_cylinder", "empty_channel"],
help="Case to collect")
ap.add_argument("--device", type=int, default=2, help="GPU device ID")
args = ap.parse_args()
# Load Phase 0 data for f_ref / T_ref
f_ref_path = os.path.join(OUTPUT_DIR, "target_cylinder", "meta.json")
if os.path.exists(f_ref_path):
with open(f_ref_path, "r") as f:
phase0_data = json.load(f)
else:
phase0_data = {"T_ref": 15000.0, "f_ref": 6.67e-5}
print("WARNING: Phase 0 not found, using default T_ref=15000")
t0 = time.time()
results = {}
if args.case in ("all", "illusion"):
print("=" * 60)
print("Collecting Illusion case...")
print("=" * 60)
phase0_data["illusion_2u"] = True
results["illusion"] = collect_illusion(args.device, phase0_data)
if args.case in ("all", "cloak"):
print("=" * 60)
print("Collecting Cloak case...")
print("=" * 60)
results["cloak"] = collect_cloak(args.device, phase0_data)
if args.case in ("all", "uncontrolled"):
print("=" * 60)
print("Collecting Uncontrolled case...")
print("=" * 60)
results["uncontrolled"] = collect_uncontrolled(args.device, phase0_data)
if args.case in ("all", "target_cylinder"):
print("=" * 60)
print("Collecting Target Cylinder...")
print("=" * 60)
results["target_cylinder"] = collect_target_cylinder(
args.device, phase0_data)
if args.case in ("all", "empty_channel"):
print("=" * 60)
print("Collecting Empty Channel (steady target)...")
print("=" * 60)
results["empty_channel"] = collect_empty_channel(args.device)
elapsed = time.time() - t0
print(f"\nPhase 1 complete in {elapsed:.1f}s")
return 0
if __name__ == "__main__":
sys.exit(main())
src/CCD_analysis/scripts/phase2_resample.py
+514
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# CCD_analysis/scripts/phase2_resample.py
"""Phase 2: Period detection and phase resampling for periodic cases.
Usage::
python phase2_resample.py
Output::
- output/resampled/ — phase-resampled data for each qualifying case
- Console report of period stability for all periodic cases
"""
from __future__ import annotations
import json
import os
import sys
import time
import numpy as np
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from scripts.cfg import ( # noqa: E402
OUTPUT_DIR, U0, SAMPLE_INTERVAL, SAMPLE_INTERVAL_ILLUSION,
N_TARGET_CYCLES, N_PTS_PER_CYCLE, TOTAL_PHASE_FRAMES,
)
from scripts.analysis_utils import ( # noqa: E402
detect_dominant_frequency, detect_cycle_stability, phase_resample,
)
# ---------------------------------------------------------------------------
# Gate criteria
# ---------------------------------------------------------------------------
CV_T_THRESHOLD_STRICT = 0.10
CV_T_THRESHOLD_RELAXED = 0.12
DELTA_F_THRESHOLD_STRICT = 0.10
DELTA_F_THRESHOLD_RELAXED = 0.20
# ---------------------------------------------------------------------------
# Load raw data for a case
# ---------------------------------------------------------------------------
def load_case_raw(case_name: str) -> dict:
"""Load raw sensor/force/action data for a case."""
case_dir = os.path.join(OUTPUT_DIR, case_name)
meta_path = os.path.join(case_dir, "meta.json")
if not os.path.exists(meta_path):
return {"exists": False, "error": f"{case_dir}/meta.json not found"}
with open(meta_path, "r") as f:
meta = json.load(f)
result = {"exists": True, "meta": meta, "name": case_name}
# Load sensors (check both naming conventions)
sens_path = os.path.join(case_dir, "sensors.npz")
dense_sens_path = os.path.join(case_dir, "dense_sensors.npz")
raw_sens_path = os.path.join(case_dir, "raw_sensors.npz")
if os.path.exists(dense_sens_path):
load_path = dense_sens_path
elif os.path.exists(sens_path):
load_path = sens_path
elif os.path.exists(raw_sens_path):
load_path = raw_sens_path
else:
load_path = None
if load_path is not None:
data = np.load(load_path)
if "sensors" in data:
result["sensors"] = data["sensors"]
if "forces" in data:
result["forces"] = data["forces"]
if "actions" in data:
result["actions"] = data["actions"]
# Determine sample interval (dense data may use dense_dt)
if "dense_dt" in data:
result["sample_interval"] = int(data["dense_dt"])
elif "sample_interval" in data:
result["sample_interval"] = int(data["sample_interval"])
# If we loaded dense data but missing actions, try sensors.npz
if load_path == dense_sens_path and "actions" not in result:
if os.path.exists(sens_path):
extra = np.load(sens_path)
if "actions" in extra:
result["actions"] = extra["actions"]
print(f" loaded actions from sensors.npz: {result['actions'].shape}")
# Determine sample interval from meta if not in data
if "sample_interval" not in result:
result["sample_interval"] = meta.get("sample_interval", SAMPLE_INTERVAL)
# Get U0 from meta
result["U0"] = meta.get("U0", 0.01)
# Load fields (lazy — only when accessed)
fields_path = os.path.join(case_dir, "fields.npz")
if os.path.exists(fields_path):
result["_fields_path"] = fields_path
result["_fields_loader"] = lambda p=fields_path: np.load(p)
return result
# ---------------------------------------------------------------------------
# Check period stability for a case
# ---------------------------------------------------------------------------
def check_period_stability(
case_data: dict, f_ref: float, T_ref: float, St: float = 0.2667,
case_U0: float = 0.01
) -> dict:
"""Check period stability and return gate result.
delta_f computed relative to expected frequency from Strouhal number:
f_expected = St * U0 / D_cylinder
This handles cases at different U0 (e.g. illusion 2U at U0=0.02).
Returns dict with gate info.
"""
sensors = case_data.get("sensors")
if sensors is None or len(sensors) < 30:
return {"gate": "no_data", "reason": "insufficient sensor data"}
sample_interval = case_data.get("sample_interval", SAMPLE_INTERVAL)
D_cyl = 40.0 # cylinder diameter in lattice units (2*L0)
signal = sensors[:, 3]
# Detect frequency
f_case, T_case, peak_power = detect_dominant_frequency(signal, sample_interval)
# Detect cycle stability
cv_T, mean_T, cycle_lengths = detect_cycle_stability(signal, sample_interval)
# Expected frequency from Strouhal number at this U0
f_expected = St * case_U0 / D_cyl
# Gate — compare to expected frequency from target cylinder St
delta_f = abs(f_case - f_expected) / f_expected if f_expected > 0 else 1.0
# Gate determination with new thresholds
if cv_T <= CV_T_THRESHOLD_STRICT and delta_f <= DELTA_F_THRESHOLD_STRICT:
gate = "strict"
elif cv_T <= CV_T_THRESHOLD_RELAXED and delta_f <= DELTA_F_THRESHOLD_RELAXED:
gate = "relaxed"
else:
gate = "auxiliary"
# Interpolation quality check
N_raw_per_cycle = mean_T / sample_interval if mean_T > 0 else 0
rho_interp = 24.0 / N_raw_per_cycle if N_raw_per_cycle > 0 else 99.0
if rho_interp > 2.0:
interp_quality = "reject"
elif rho_interp > 1.5:
interp_quality = "borderline"
elif rho_interp > 1.2:
interp_quality = "acceptable"
else:
interp_quality = "ideal"
# Also check signal quality
signal_range = float(np.max(signal) - np.min(signal))
signal_rms = float(np.std(signal))
result = {
"case": case_data.get("name", "unknown"),
"gate": gate,
"f_case": float(f_case),
"f_expected": float(f_expected),
"T_case": float(T_case),
"T_case_samples": float(T_case / sample_interval),
"CV_T": float(cv_T),
"delta_f": float(delta_f),
"mean_T_samples": float(mean_T / sample_interval),
"N_raw_per_cycle": float(N_raw_per_cycle),
"rho_interp": float(rho_interp),
"interp_quality": interp_quality,
"n_cycles_detected": len(cycle_lengths),
"signal_range": signal_range,
"signal_rms": signal_rms,
"n_raw_samples": len(sensors),
"St": St,
"U0": float(case_U0),
}
return result
# ---------------------------------------------------------------------------
# Extract cycles and resample
# ---------------------------------------------------------------------------
def extract_and_resample(
case_data: dict, f_ref: float, T_ref: float, St: float = 0.2667,
n_cycles: int = N_TARGET_CYCLES,
n_pts: int = N_PTS_PER_CYCLE,
) -> dict:
"""Extract cycles and resample to uniform phase grid.
Parameters
----------
case_data : dict
Raw case data with sensors, forces, actions, ux, uy.
f_ref, T_ref : float
Reference frequency and period (from Phase 0 at U0=0.01).
St : float
Strouhal number (U0-invariant reference).
n_cycles, n_pts : int
Number of cycles and points per cycle.
Returns
-------
dict with resampled fields, sensors, forces, actions.
"""
sensors = case_data.get("sensors")
sample_interval = case_data.get("sample_interval", SAMPLE_INTERVAL)
case_U0 = case_data.get("U0", 0.01)
D_cyl = 40.0
# Compute expected T for this case's U0
f_expected = St * case_U0 / D_cyl
T_expected = 1.0 / f_expected if f_expected > 0 else T_ref
if sensors is None or len(sensors) < 30:
return {"resampled": False, "reason": "insufficient data"}
signal = sensors[:, 3] # centre sensor v
# Find rising zero-crossings to define cycle boundaries
y = signal - np.mean(signal)
sign = np.sign(y)
crossings = np.where((sign[:-1] < 0) & (sign[1:] > 0))[0]
if len(crossings) < n_cycles + 1:
print(f" Only {len(crossings)} crossings found, need {n_cycles + 1}")
return {"resampled": False, "reason": f"need {n_cycles + 1} crossings, got {len(crossings)}"}
# Select the most representative n_cycles (use expected period)
cycle_lengths = np.diff(crossings)
T_exp_samples = T_expected / sample_interval
# Score each window of n_cycles consecutive cycles
best_score = float("inf")
best_start = 0
for i in range(len(cycle_lengths) - n_cycles + 1):
window = cycle_lengths[i:i + n_cycles]
score = np.sum((window - T_exp_samples) ** 2)
if score < best_score:
best_score = score
best_start = i
selected_crossings = list(crossings[best_start:best_start + n_cycles + 1])
# Phase resample sensors
if sensors.ndim == 2:
resampled_sensors = phase_resample(
sensors, selected_crossings, n_pts=n_pts
)
else:
resampled_sensors = phase_resample(
sensors[:, None], selected_crossings, n_pts=n_pts
)
result = {
"resampled": True,
"selected_crossings": selected_crossings,
"sensors": resampled_sensors, # (n_cycles, n_pts, 6)
}
# Resample forces
forces = case_data.get("forces")
if forces is not None and forces.ndim == 2:
result["forces"] = phase_resample(
forces, selected_crossings, n_pts=n_pts
)
# Resample actions
actions = case_data.get("actions")
if actions is not None and actions.ndim == 2:
result["actions"] = phase_resample(
actions, selected_crossings, n_pts=n_pts
)
# Resample field data (lazy-loaded if available)
ux = None
uy = None
loader = case_data.get("_fields_loader")
if loader is not None:
fields_npz = loader()
if "ux" in fields_npz and "uy" in fields_npz:
ux = fields_npz["ux"]
uy = fields_npz["uy"]
if ux is not None and uy is not None:
n_fields = len(ux)
n_sensors = len(sensors)
# Fields and sensors are sampled at different rates
# Fields are saved at save_interval within each PPO step
# Sensors are saved once per PPO step
# The ratio is approximately T_ref / n_pts / sample_interval
# Convert crossing indices from sensor-space to field-space
# Simple approach: resample fields using the same crossing indices
# Since fields may have different count, use normalized indices
field_crossings = [
int(c * n_fields / n_sensors) for c in selected_crossings
]
field_crossings = [max(0, min(c, n_fields - 1)) for c in field_crossings]
# Deduplicate and ensure > n_cycles+1 entries
field_crossings = sorted(set(field_crossings))
if len(field_crossings) >= 2:
# Stack ux and uy into single array
nx, ny = ux.shape[1], ux.shape[2]
field_flat = np.column_stack([
ux.reshape(n_fields, -1), uy.reshape(n_fields, -1)
])
resampled_fields = phase_resample(
field_flat, field_crossings[:n_cycles + 1], n_pts=n_pts
)
# Unflatten
n_cycle_actual, n_pt, n_dim = resampled_fields.shape
half = n_dim // 2
result["ux"] = resampled_fields[:, :, :half].reshape(
n_cycle_actual, n_pt, ny, nx
)
result["uy"] = resampled_fields[:, :, half:].reshape(
n_cycle_actual, n_pt, ny, nx
)
return result
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
# Load Phase 0 reference data
ref_path = os.path.join(OUTPUT_DIR, "target_cylinder", "meta.json")
if not os.path.exists(ref_path):
print("ERROR: Phase 0 data not found. Run phase0_standard_freq.py first.")
return 1
with open(ref_path, "r") as f:
ref = json.load(f)
f_ref = ref["f_ref"]
T_ref = ref["T_ref"]
print(f"Reference: f_ref={f_ref:.6f}, T_ref={T_ref:.0f} steps, St={ref['St']:.4f}")
print()
# Load all periodic cases
periodic_cases = ["target_cylinder", "illusion", "uncontrolled"]
all_raw = {}
for case in periodic_cases:
print(f"Loading {case}...")
all_raw[case] = load_case_raw(case)
if all_raw[case].get("exists"):
nf = "lazy"
ns = len(all_raw[case].get("sensors", []))
print(f" fields={nf}, sensors={ns}")
# Check period stability
print("\n=== Period Stability Check ===")
results = []
for case in periodic_cases:
data = all_raw[case]
if not data.get("exists"):
print(f" {case}: NO DATA, skipping")
continue
r = check_period_stability(
data, f_ref, T_ref, St=ref["St"],
case_U0=data.get("U0", 0.01),
)
results.append(r)
status = r["gate"].upper()
print(f" {case}: {status} f_case={r['f_case']:.6f} "
f"CV_T={r['CV_T']:.4f} delta_f={r['delta_f']:.4f} "
f"T_samples={r['mean_T_samples']:.1f} "
f"N_raw/cycle={r.get('N_raw_per_cycle', '?'):.1f} "
f"interp={r.get('interp_quality', '?')}")
# Save stability report
os.makedirs(os.path.join(OUTPUT_DIR, "resampled"), exist_ok=True)
with open(os.path.join(OUTPUT_DIR, "resampled", "stability_report.json"), "w") as f:
json.dump({
"f_ref": f_ref,
"T_ref": T_ref,
"St": ref["St"],
"thresholds": {
"CV_T_strict": CV_T_THRESHOLD_STRICT,
"CV_T_relaxed": CV_T_THRESHOLD_RELAXED,
"delta_f_strict": DELTA_F_THRESHOLD_STRICT,
"delta_f_relaxed": DELTA_F_THRESHOLD_RELAXED,
},
"cases": results,
}, f, indent=2)
# Phase resample for qualifying cases
print("\n=== Phase Resampling ===")
qualifying = [r for r in results if r.get("gate") in ("strict", "relaxed")]
falling = [r for r in results if r.get("gate") not in ("strict", "relaxed")]
strict_cases = [r["case"] for r in results if r.get("gate") == "strict"]
relaxed_cases = [r["case"] for r in results if r.get("gate") == "relaxed"]
print(f"Strict (main POD basis): {strict_cases}")
print(f"Relaxed (projection): {relaxed_cases}")
print(f"Auxiliary/falling: {[r['case'] for r in falling]}")
for r in qualifying:
case_name = r["case"]
print(f"\nResampling {case_name} (strict)...")
data = all_raw[case_name]
result = extract_and_resample(data, f_ref, T_ref, St=ref["St"])
if result.get("resampled"):
out_dir = os.path.join(OUTPUT_DIR, "resampled", case_name)
os.makedirs(out_dir, exist_ok=True)
# Save resampled data
save_dict = {
"sensors": result["sensors"], # (n_cycles, n_pts, 6)
"n_cycles": result["sensors"].shape[0],
"n_pts": result["sensors"].shape[1],
}
if "forces" in result:
save_dict["forces"] = result["forces"]
if "actions" in result:
save_dict["actions"] = result["actions"]
if "ux" in result and "uy" in result:
save_dict["ux"] = result["ux"]
save_dict["uy"] = result["uy"]
np.savez_compressed(os.path.join(out_dir, "resampled.npz"), **save_dict)
# Also save metadata
meta = {
"case": case_name,
"f_ref": f_ref,
"T_ref": T_ref,
"n_cycles": int(result["sensors"].shape[0]),
"n_pts": int(result["sensors"].shape[1]),
"selected_crossings": [int(c) for c in result["selected_crossings"]],
"has_fields": "ux" in result,
}
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(meta, f, indent=2)
sh = result["sensors"].shape
print(f" Resampled: {sh}, fields={'yes' if meta['has_fields'] else 'no'}")
else:
print(f" Resampling failed: {result.get('reason', 'unknown')}")
# Also resample relaxed cases (Scheme A — projection only, no common POD)
# Also resample relaxed cases (projection only, no POD basis training)
for r in results:
if r.get("gate") != "relaxed":
continue
case_name = r["case"]
if case_name in [q["case"] for q in qualifying]:
continue # already done above
print(f"\nResampling {case_name} (relaxed, projection)...")
data = all_raw[case_name]
result = extract_and_resample(data, f_ref, T_ref, St=ref["St"])
if result.get("resampled"):
out_dir = os.path.join(OUTPUT_DIR, "resampled", case_name)
os.makedirs(out_dir, exist_ok=True)
save_dict = {
"sensors": result["sensors"],
"n_cycles": result["sensors"].shape[0],
"n_pts": result["sensors"].shape[1],
}
if "forces" in result:
save_dict["forces"] = result["forces"]
if "actions" in result:
save_dict["actions"] = result["actions"]
if "ux" in result and "uy" in result:
save_dict["ux"] = result["ux"]
save_dict["uy"] = result["uy"]
np.savez_compressed(os.path.join(out_dir, "resampled.npz"), **save_dict)
meta = {"case": case_name, "f_ref": f_ref, "T_ref": T_ref,
"n_cycles": int(result["sensors"].shape[0]),
"n_pts": int(result["sensors"].shape[1]),
"selected_crossings": [int(c) for c in result["selected_crossings"]],
"gate": "relaxed"}
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(meta, f, indent=2)
print(f" Resampled (relaxed): {result['sensors'].shape}")
else:
print(f" Resampling failed: {result.get('reason', 'unknown')}")
# Save resampling summary
summary = {"strict": strict_cases,
"relaxed": relaxed_cases,
"auxiliary": [r["case"] for r in results if r.get("gate") not in ("strict", "relaxed")]}
with open(os.path.join(OUTPUT_DIR, "resampled", "summary.json"), "w") as f:
json.dump(summary, f, indent=2)
print("\nPhase 2 complete.")
return 0
if __name__ == "__main__":
sys.exit(main())
src/CCD_analysis/scripts/phase3_pod.py
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# CCD_analysis/scripts/phase3_pod.py
"""Phase 3: Reference POD basis on phase-resampled periodic cases.
Builds POD basis from strict-qualifying cases (target_cylinder + illusion).
Non-qualifying cases (uncontrolled) are projected onto this basis.
Output::
- output/pod/reference_pod_results.npz
- output/pod/reference_pod_metrics.json
"""
from __future__ import annotations
import json
import os
import sys
import time
import numpy as np
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from scripts.cfg import OUTPUT_DIR, NX, NY
from scripts.analysis_utils import (
compute_pod, cumulative_energy, e95_index,
stack_velocity_fields, unstack_velocity_modes,
)
R_CANDIDATES = [6, 8, 10] # POD truncation levels to test
def load_resampled(case_name: str) -> dict:
"""Load resampled data for a case. Returns dict or None."""
resample_dir = os.path.join(OUTPUT_DIR, "resampled", case_name)
data_path = os.path.join(resample_dir, "resampled.npz")
meta_path = os.path.join(resample_dir, "meta.json")
if not os.path.exists(data_path):
print(f" {case_name}: no resampled data at {data_path}")
return None
data = np.load(data_path)
meta = {}
if os.path.exists(meta_path):
with open(meta_path) as f:
meta = json.load(f)
return {"data": data, "meta": meta, "name": case_name}
def main():
print("=== Phase 3: Common POD ===\n")
# Load summary from Phase 2
summary_path = os.path.join(OUTPUT_DIR, "resampled", "summary.json")
if not os.path.exists(summary_path):
print("ERROR: Run Phase 2 first.")
return 1
with open(summary_path) as f:
summary = json.load(f)
strict_cases = summary.get("strict", [])
relaxed_cases = summary.get("relaxed", [])
failed_cases = summary.get("failed", [])
print(f" Strict: {strict_cases}")
print(f" Relaxed (projected): {relaxed_cases}")
print(f" Failed: {failed_cases}")
if not strict_cases:
print("ERROR: No strict-qualifying cases for common POD.")
return 1
# Load all resampled data
all_data = {}
for case in strict_cases + relaxed_cases:
d = load_resampled(case)
if d is not None:
all_data[case] = d
# Build snapshot matrix from strict cases
print("\nBuilding common POD snapshot matrix...")
snapshots = []
case_ranges = {} # {case_name: (start_idx, end_idx)}
current_idx = 0
for case in strict_cases:
if case not in all_data:
continue
data = all_data[case]["data"]
ux = data.get("ux")
uy = data.get("uy")
if ux is None or uy is None:
print(f" WARNING: {case} has no field data, skipping")
continue
# Flatten each resampled snapshot
n_cycles, n_pts = ux.shape[0], ux.shape[1]
for c in range(n_cycles):
for p in range(n_pts):
q = np.concatenate([
ux[c, p].ravel(),
uy[c, p].ravel(),
])
snapshots.append(q)
case_ranges[case] = (current_idx, current_idx + n_cycles * n_pts)
current_idx += n_cycles * n_pts
print(f" {case}: {n_cycles}x{n_pts} = {n_cycles*n_pts} snapshots")
if not snapshots:
print("ERROR: No field data for POD.")
return 1
Q = np.column_stack(snapshots) # (2*nx*ny, N)
print(f" Snapshot matrix: {Q.shape[0]} x {Q.shape[1]}")
# Compute POD
print("\nComputing POD...")
mean_field, modes, s, coeffs = compute_pod(Q)
energy = cumulative_energy(s)
e95 = e95_index(energy)
print(f" Modes: {len(s)}")
print(f" E95 = {e95}")
for i in range(min(10, len(s))):
print(f" mode {i+1}: energy={energy[i]:.4f}, sigma={s[i]:.4e}")
# Project relaxed cases onto the POD basis
projection_coeffs = {} # {case_name: coeffs_matrix}
for case in relaxed_cases:
if case not in all_data:
continue
data = all_data[case]["data"]
ux = data.get("ux")
uy = data.get("uy")
if ux is None or uy is None:
print(f" WARNING: {case} has no field data for projection")
continue
proj_snapshots = []
n_cycles, n_pts = ux.shape[0], ux.shape[1]
for c in range(n_cycles):
for p in range(n_pts):
q = np.concatenate([ux[c, p].ravel(), uy[c, p].ravel()])
proj_snapshots.append(q)
Q_proj = np.column_stack(proj_snapshots)
# Remove mean field (from strict POD)
Q_proj_centered = Q_proj - mean_field[:, None]
# Project: coefficients = modes^T @ Q
coeffs_proj = modes[:, :R_CANDIDATES[-1]].T @ Q_proj_centered
projection_coeffs[case] = coeffs_proj
print(f" {case}: projected {Q_proj.shape[1]} snapshots")
# Compute case centroids in a1-a2 phase space
print("\nCase centroids (a1, a2):")
centroids = {}
for case in strict_cases:
if case not in case_ranges:
continue
start, end = case_ranges[case]
a1 = np.mean(coeffs[0, start:end])
a2 = np.mean(coeffs[1, start:end])
centroids[case] = [float(a1), float(a2)]
print(f" {case}: a1={a1:.4f}, a2={a2:.4f}")
for case, coeffs_p in projection_coeffs.items():
a1 = np.mean(coeffs_p[0])
a2 = np.mean(coeffs_p[1])
centroids[case] = [float(a1), float(a2)]
print(f" {case}: a1={a1:.4f}, a2={a2:.4f}")
# Save results
out_dir = os.path.join(OUTPUT_DIR, "pod")
os.makedirs(out_dir, exist_ok=True)
# POD results
np.savez_compressed(os.path.join(out_dir, "pod_results.npz"),
mean_field=mean_field,
modes=modes[:, :R_CANDIDATES[-1]],
singular_values=s,
coefficients=coeffs[:R_CANDIDATES[-1]],
energy_ratio=energy[:R_CANDIDATES[-1]],
)
# Save projection coefficients for relaxed cases
for case, c in projection_coeffs.items():
np.savez(os.path.join(out_dir, f"projection_{case}.npz"),
coefficients=c)
# Metrics
pod_metrics = {
"n_total_modes": len(s),
"E95": int(e95),
"energy_first_2": float(energy[1]) if len(energy) > 1 else float(energy[0]),
"energy_first_6": float(energy[min(5, len(energy)-1)]),
"singular_values": [float(v) for v in s[:R_CANDIDATES[-1]]],
"energy_ratio": [float(v) for v in energy[:R_CANDIDATES[-1]]],
"case_centroids": centroids,
"case_ranges": {k: [int(v[0]), int(v[1])] for k, v in case_ranges.items()},
"n_strict_cases": len(strict_cases),
"strict_cases": strict_cases,
"relaxed_cases": relaxed_cases,
}
with open(os.path.join(out_dir, "pod_metrics.json"), "w") as f:
json.dump(pod_metrics, f, indent=2)
print(f"\nResults saved to {out_dir}")
print("Phase 3 complete.")
return 0
if __name__ == "__main__":
sys.exit(main())
src/CCD_analysis/scripts/phase4_ccd.py
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# CCD_analysis/scripts/phase4_ccd.py
"""Phase 4: CCD analysis on POD coefficients.
Computes:
- force-CCD (all periodic cases): total Fx, Fy
- action-CCD (illusion only): [Omega_front, Omega_bottom, Omega_top]
- signature-CCD (illusion only): e_s(t+tau_c)
- Modal overlap O_k between case pairs
"""
from __future__ import annotations
import json
import os
import sys
import numpy as np
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from scripts.cfg import OUTPUT_DIR
from scripts.analysis_utils import compute_reduced_ccd, cumulative_energy
R_CANDIDATES = [6, 8, 10]
CCD_Q = 12
def load_resampled_coeffs(case_name: str, r: int) -> np.ndarray:
"""Load POD coefficients from projection or strict POD."""
proj_path = os.path.join(OUTPUT_DIR, "pod", f"projection_{case_name}.npz")
if os.path.exists(proj_path):
data = np.load(proj_path)
return data["coefficients"][:r]
pod_path = os.path.join(OUTPUT_DIR, "pod", "pod_results.npz")
metrics_path = os.path.join(OUTPUT_DIR, "pod", "pod_metrics.json")
if os.path.exists(pod_path) and os.path.exists(metrics_path):
pod = np.load(pod_path)
with open(metrics_path) as f:
metrics = json.load(f)
cr = metrics.get("case_ranges", {})
if case_name in cr:
start, end = cr[case_name]
return pod["coefficients"][:r, start:end]
return None
def load_resampled_signals(case_name: str, key: str = "sensors", n_channels: int = 6) -> np.ndarray:
"""Load resampled signal and return (n_channels, N)."""
path = os.path.join(OUTPUT_DIR, "resampled", case_name, "resampled.npz")
if not os.path.exists(path):
return None
data = np.load(path)
arr = data.get(key)
if arr is None:
return None
if key == "forces":
arr_2d = arr.reshape(-1, arr.shape[-1])
if arr_2d.shape[1] == 2:
return arr_2d.T
f = arr_2d
Fx = (f[:, 0] + f[:, 2] + f[:, 4])
Fy = (f[:, 1] + f[:, 3] + f[:, 5])
return np.vstack([Fx, Fy])
if key == "actions":
return arr.reshape(-1, arr.shape[-1]).T
return arr.reshape(-1, arr.shape[-1]).T
def compute_ccd_metrics(case: str, coeffs: np.ndarray, observable: np.ndarray, obs_name: str) -> dict:
"""Compute CCD metrics for a single case-observable pair."""
N = coeffs.shape[1]
N_train = N * 3 // 4
a_train = coeffs[:, :N_train]
a_test = coeffs[:, N_train:]
y_train = observable[:, :N_train]
y_test = observable[:, N_train:]
r = coeffs.shape[0]
# CCD
W, sigma, z = compute_reduced_ccd(coeffs, observable, Q_delay=CCD_Q)
ccd_ene = cumulative_energy(sigma)
m80 = int(np.searchsorted(ccd_ene, 0.80) + 1) if len(ccd_ene) > 0 else 0
# POD regression
W_pod = y_train @ a_train.T @ np.linalg.pinv(a_train @ a_train.T + 1e-8 * np.eye(r))
y_pred_pod = W_pod @ a_test
y_test_r = y_test.ravel()
y_pred_pod_r = y_pred_pod.ravel()
if np.std(y_test_r) > 1e-12 and np.std(y_pred_pod_r) > 1e-12:
corr_pod = float(np.corrcoef(y_pred_pod_r, y_test_r)[0, 1])
else:
corr_pod = 0.0
# CCD regression
n_ccd = min(r, z.shape[0])
z_train = z[:n_ccd, :N_train]
z_test = z[:n_ccd, N_train:]
W_reg_ccd = y_train @ z_train.T @ np.linalg.pinv(z_train @ z_train.T + 1e-8 * np.eye(n_ccd))
y_pred_ccd = W_reg_ccd @ z_test
y_pred_ccd_r = y_pred_ccd.ravel()
if np.std(y_test_r) > 1e-12 and np.std(y_pred_ccd_r) > 1e-12:
corr_ccd = float(np.corrcoef(y_pred_ccd_r, y_test_r)[0, 1])
else:
corr_ccd = 0.0
# Top CCD mode correlations with observable
n_corr = min(5, len(sigma))
corr_z = []
for k in range(n_corr):
zk_train = z[k, :N_train]
if np.std(zk_train) > 1e-12:
rho = float(np.corrcoef(zk_train, observable[0, :N_train])[0, 1])
else:
rho = 0.0
corr_z.append(rho)
return {
"case": case,
"observable": obs_name,
"r": r,
"sigma": [float(s) for s in sigma[:n_corr]],
"ccd_energy": [float(e) for e in ccd_ene[:n_corr]],
"m80": int(m80),
"corr_POD_obs": corr_pod,
"corr_CCD_obs": corr_ccd,
"corr_z_top5": corr_z,
"N_total": int(N),
"N_train": int(N_train),
"N_test": int(N - N_train),
}
def compute_modal_overlap(W_dict: dict, case_pairs: list) -> dict:
"""Compute modal overlap O_k between cases.
O_k(A, B) = |W[:,k]_A^T @ W[:,k]_B|
Higher means the k-th CCD direction is more aligned between cases.
"""
overlap = {}
for case_a, case_b in case_pairs:
if case_a not in W_dict or case_b not in W_dict:
continue
Wa = W_dict[case_a]
Wb = W_dict[case_b]
n = min(Wa.shape[1], Wb.shape[1])
pair_key = f"O_{case_a}_{case_b}"
pair_vals = []
for k in range(min(n, 5)):
ak = Wa[:, k] / (np.linalg.norm(Wa[:, k]) + 1e-12)
bk = Wb[:, k] / (np.linalg.norm(Wb[:, k]) + 1e-12)
pair_vals.append(float(abs(ak @ bk)))
overlap[pair_key] = pair_vals
return overlap
def load_target_signals(case_name: str) -> np.ndarray:
"""Load target_cylinder resampled sensors as reference signature.
Since target_cylinder is at U0=0.01 and illusion at U0=0.02,
signals are amplitude-normalized by their respective RMS.
"""
# Use target_cylinder resampled sensors as reference
ref_path = os.path.join(OUTPUT_DIR, "resampled", "target_cylinder", "resampled.npz")
if not os.path.exists(ref_path):
return None
ref = np.load(ref_path)
sensors_ref = ref.get("sensors") # (n_cycles, n_pts, 6)
if sensors_ref is None:
return None
return sensors_ref.reshape(-1, sensors_ref.shape[-1]).T # (6, N)
def main():
print("=== Phase 4: CCD Analysis ===\n")
metrics_path = os.path.join(OUTPUT_DIR, "pod", "pod_metrics.json")
if not os.path.exists(metrics_path):
print("ERROR: Run Phase 3 first.")
return 1
with open(metrics_path) as f:
pod_metrics = json.load(f)
all_cases = pod_metrics.get("strict_cases", []) + pod_metrics.get("relaxed_cases", [])
all_results = {}
W_dict = {} # {case_observable_r: W_matrix}
for r in R_CANDIDATES:
print(f"\n{'='*60}")
print(f"POD truncation r={r}")
print(f"{'='*60}")
# ----- 1. Force-CCD (all cases) -----
print("\n--- Force-CCD ---")
for case in all_cases:
coeffs = load_resampled_coeffs(case, r)
if coeffs is None:
continue
y_force = load_resampled_signals(case, "forces", 2)
if y_force is None:
print(f" {case}: no force data")
continue
if y_force.shape[-1] != coeffs.shape[1]:
print(f" {case}: force length mismatch, skipping")
continue
# CCD computation
W, sigma, z = compute_reduced_ccd(coeffs, y_force, Q_delay=CCD_Q)
ccd_ene = cumulative_energy(sigma)
m80 = int(np.searchsorted(ccd_ene, 0.80) + 1)
key = f"{case}_force_r{r}"
W_dict[key] = W
all_results[key] = compute_ccd_metrics(case, coeffs, y_force, "force_CCD")
print(f" {case}: m80={all_results[key]['m80']}, "
f"sigma={sigma[0]:.3f},{sigma[1]:.3f}")
# ----- 2. Action-CCD (illusion only) -----
print("\n--- Action-CCD (illusion only) ---")
if "illusion" in all_cases:
coeffs = load_resampled_coeffs("illusion", r)
if coeffs is not None:
y_act = load_resampled_signals("illusion", "actions", 3)
if y_act is not None and y_act.shape[-1] == coeffs.shape[1]:
key = f"illusion_action_r{r}"
W, sigma, z = compute_reduced_ccd(coeffs, y_act, Q_delay=CCD_Q)
W_dict[key] = W
all_results[key] = compute_ccd_metrics(
"illusion", coeffs, y_act, "action_CCD")
print(f" illusion: m80={all_results[key]['m80']}, "
f"sigma={sigma[0]:.3f},{sigma[1]:.3f}")
else:
print(f" illusion: no action data (y={y_act.shape if y_act is not None else None}, "
f"N={coeffs.shape[1]})")
# ----- 3. Signature-CCD (illusion only, tau_c=0) -----
print("\n--- Signature-CCD (illusion only, tau_c=0) ---")
if "illusion" in all_cases:
coeffs = load_resampled_coeffs("illusion", r)
if coeffs is not None:
# Load actual sensors
sensors = load_resampled_signals("illusion", "sensors", 6)
# Load target sensors
target_sensors = load_target_signals("illusion")
if sensors is not None and target_sensors is not None and sensors.shape == target_sensors.shape:
# e_s(t) = s(t) - s_tar(t)
e_s = sensors - target_sensors
key = f"illusion_signature_r{r}"
W, sigma, z = compute_reduced_ccd(coeffs, e_s, Q_delay=CCD_Q)
W_dict[key] = W
all_results[key] = compute_ccd_metrics(
"illusion", coeffs, e_s, "signature_CCD")
print(f" illusion: m80={all_results[key]['m80']}, "
f"sigma={sigma[0]:.3f},{sigma[1]:.3f}")
else:
print(f" illusion: signature data issue "
f"(sensors={sensors.shape if sensors is not None else None}, "
f"target={target_sensors.shape if target_sensors is not None else None})")
# ----- Modal Overlap O_k -----
print("\n--- Modal Overlap O_k ---")
all_obs_keys = [k for k in all_results.keys()]
pair_results = {}
for r in R_CANDIDATES:
# Force-CCD overlap between strict cases
strict_cases = pod_metrics.get("strict_cases", [])
for i, ca in enumerate(strict_cases):
for cb in strict_cases[i+1:]:
key_a = f"{ca}_force_r{r}"
key_b = f"{cb}_force_r{r}"
if key_a in W_dict and key_b in W_dict:
Wa = W_dict[key_a]
Wb = W_dict[key_b]
n = min(Wa.shape[1], Wb.shape[1], 5)
ov = []
for k in range(n):
ak = Wa[:, k] / (np.linalg.norm(Wa[:, k]) + 1e-12)
bk = Wb[:, k] / (np.linalg.norm(Wb[:, k]) + 1e-12)
ov.append(float(abs(ak @ bk)))
pk = f"O_{ca}_{cb}_force_r{r}"
pair_results[pk] = ov
print(f" {ca} vs {cb} (force, r={r}): "
f"O1={ov[0]:.4f}, O2={ov[1]:.4f}")
# Also compare illusion to target in action and signature space
if "illusion" in all_cases:
ia_key = f"illusion_action_r{r}"
is_key = f"illusion_signature_r{r}"
for tc in strict_cases:
tf_key = f"{tc}_force_r{r}"
if ia_key in W_dict and tf_key in W_dict:
Wa = W_dict[ia_key]
Wb = W_dict[tf_key]
n = min(Wa.shape[1], Wb.shape[1], 5)
ov = []
for k in range(n):
ak = Wa[:, k] / (np.linalg.norm(Wa[:, k]) + 1e-12)
bk = Wb[:, k] / (np.linalg.norm(Wb[:, k]) + 1e-12)
ov.append(float(abs(ak @ bk)))
pk = f"O_action_vs_force_{tc}_r{r}"
pair_results[pk] = ov
print(f" action vs {tc}-force (r={r}): "
f"O1={ov[0]:.4f}, O2={ov[1]:.4f}")
# Save
out_dir = os.path.join(OUTPUT_DIR, "ccd")
os.makedirs(out_dir, exist_ok=True)
all_results["modal_overlaps"] = pair_results
with open(os.path.join(out_dir, "ccd_metrics.json"), "w") as f:
json.dump(all_results, f, indent=2)
# Summary
print(f"\n{'='*70}")
print("Summary: CCD metrics")
print(f"{'='*70}")
for key in sorted([k for k in all_results.keys() if k != "modal_overlaps"]):
m = all_results[key]
sig = m.get("sigma", [0, 0])
print(f" {key:<40} m80={m['m80']} "
f"sigma=[{sig[0]:.3f},{sig[1] if len(sig)>1 else 0:.3f}] "
f"corr_CCD={m['corr_CCD_obs']:.4f}")
print(f"\nSaved to {out_dir}/ccd_metrics.json")
print("Phase 4 complete.")
return 0
if __name__ == "__main__":
sys.exit(main())
src/CCD_analysis/scripts/phase5_steady.py
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# CCD_analysis/scripts/phase5_steady.py
"""
WARNING: This script has known issues.
- E_mean_uy calculation explodes because empty_channel uy ~ 0 (denominator near zero)
- eta_fluc is negative because cloak and uncontrolled use different pinball positions
(cloak uses standard layout FRONT/BOTTOM/TOP, uncontrolled may differ)
- L_r (recirculation zone length) = 0, which may indicate incorrect u=0 detection logic
Need to verify against actual flow fields.
Use this as reference only. Do NOT use resulting metrics for conclusions without validation.
"""
from __future__ import annotations
import json
import os
import sys
import numpy as np
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from scripts.cfg import OUTPUT_DIR, NX, NY
def load_fields(case_name: str):
"""Load fields.npz for a case, return (ux, uy) as (N, NY, NX)."""
path = os.path.join(OUTPUT_DIR, case_name, "fields.npz")
if not os.path.exists(path):
return None
data = np.load(path)
return data["ux"].astype(np.float64), data["uy"].astype(np.float64)
def load_sensors(case_name: str):
"""Load sensors.npz for a case."""
path = os.path.join(OUTPUT_DIR, case_name, "sensors.npz")
if not os.path.exists(path):
return None
return np.load(path)
def compute_mean_rms(ux, uy):
"""Compute mean and RMS fields from time series."""
ux_mean = np.mean(ux, axis=0)
uy_mean = np.mean(uy, axis=0)
ux_rms = np.sqrt(np.mean((ux - ux_mean) ** 2, axis=0))
uy_rms = np.sqrt(np.mean((uy - uy_mean) ** 2, axis=0))
return ux_mean, uy_mean, ux_rms, uy_rms
def recirculation_metrics(ux_mean):
"""Extract recirculation zone from mean ux field.
Returns (L_r, A_r) where:
L_r = length of recirculation zone along centerline
A_r = area of recirculation zone (u < 0)
"""
ny, nx = ux_mean.shape
center_y = ny // 2
margin = 10
# Centerline ux
cline = ux_mean[center_y, :]
# Find u=0 crossings behind the pinball (x > 400 or so)
start_x = 400
end_x = nx - 50
roi = cline[start_x:end_x]
neg = np.where(roi < 0)[0]
if len(neg) > 0:
# Recirculation length = distance from end of negative region
neg_end = neg[-1] + start_x
L_r = float(neg_end - 400)
else:
L_r = 0.0
# Area: count cells with u < 0 in the wake region
wake_region = ux_mean[:, 300:800]
area_mask = wake_region < 0
A_r = float(np.sum(area_mask) * 1.0) # in lattice cells
return L_r, A_r
def main():
print("=== Phase 5: Cloak Steady-Line Analysis ===\n")
# Load data
cloak_fields = load_fields("cloak")
if cloak_fields is None:
print("ERROR: No cloak fields found. Run Phase 1b first.")
return 1
ux_c, uy_c = cloak_fields
channel_fields = load_fields("empty_channel")
if channel_fields is None:
print("ERROR: No empty_channel fields found. Run Phase 1d first.")
return 1
ux_ch, uy_ch = channel_fields
unc_fields = load_fields("uncontrolled")
if unc_fields is None:
print("WARNING: No uncontrolled fields for comparison.")
unc_fields = None
cloak_sens = load_sensors("cloak")
if cloak_sens is None:
print("ERROR: No cloak sensors found.")
return 1
# Compute mean and RMS
ux_c_mean, uy_c_mean, ux_c_rms, uy_c_rms = compute_mean_rms(ux_c, uy_c)
ux_ch_mean, uy_ch_mean, _, _ = compute_mean_rms(ux_ch, uy_ch)
# 1. E_mean: mean flow relative error (vs empty channel), normalized by U0
ux_err = np.mean((ux_c_mean - ux_ch_mean) ** 2)
ux_ref = np.mean(ux_ch_mean ** 2) + 1e-12
E_mean_ux = float(ux_err / ux_ref)
E_mean_uy = float(np.mean((uy_c_mean - uy_ch_mean) ** 2)) / (np.mean(uy_ch_mean ** 2) + 1e-12)
E_mean = (E_mean_ux + E_mean_uy) / 2.0
print(f"1. E_mean (rel. to empty channel):")
print(f" ux error={E_mean_ux:.4f}, uy error={E_mean_uy:.4f}, avg={E_mean:.4f}")
# 2. E_sensor_mean
sensors = cloak_sens["sensors"]
forces = cloak_sens["forces"]
sensor_mean = np.mean(sensors, axis=0)
# Target empty channel: U0=0.01 parabolic, sensor ux should be near U0, uy near 0
sensor_target_mean = np.array([1.0, 0.0, 1.0, 0.0, 1.0, 0.0], dtype=np.float32)
E_sensor_mean = float(np.mean(np.abs(sensor_mean - sensor_target_mean)))
print(f"2. E_sensor_mean = {E_sensor_mean:.4f} "
f"(sensor_mean={sensor_mean[:3].tolist()})")
# 3. eta_fluc: fluctuation suppression ratio
if unc_fields is not None:
ux_unc, uy_unc = unc_fields
# Use first 30 frames of uncontrolled for fair comparison
n_compare = min(len(ux_c), len(ux_unc), 30)
ux_c_sub, uy_c_sub = ux_c[:n_compare], uy_c[:n_compare]
ux_unc_sub, uy_unc_sub = ux_unc[:n_compare], uy_unc[:n_compare]
# RMS in wake region only (300-800, full height)
wake_x_start, wake_x_end = 300, 800
ux_c_mean_s, uy_c_mean_s = np.mean(ux_c_sub, axis=0), np.mean(uy_c_sub, axis=0)
ux_unc_mean_s, uy_unc_mean_s = np.mean(ux_unc_sub, axis=0), np.mean(uy_unc_sub, axis=0)
rms_c = np.sqrt(np.mean((ux_c_sub - ux_c_mean_s) ** 2 + (uy_c_sub - uy_c_mean_s) ** 2, axis=0))
rms_unc = np.sqrt(np.mean((ux_unc_sub - ux_unc_mean_s) ** 2 + (uy_unc_sub - uy_unc_mean_s) ** 2, axis=0))
# Integrate RMS over wake region
int_rms_c = float(np.sum(rms_c[:, wake_x_start:wake_x_end]))
int_rms_unc = float(np.sum(rms_unc[:, wake_x_start:wake_x_end]))
eps = 1e-12
eta_fluc = 1.0 - int_rms_c / (int_rms_unc + eps)
print(f"3. eta_fluc = {eta_fluc:.4f} (wake region, first {n_compare} frames)")
else:
eta_fluc = 0.0
print("3. eta_fluc: skipped (no uncontrolled data)")
# 4. Recirculation zone
L_r_c, A_r_c = recirculation_metrics(ux_c_mean)
if unc_fields is not None:
ux_unc_mean = np.mean(ux_unc_sub, axis=0)
L_r_unc, A_r_unc = recirculation_metrics(ux_unc_mean)
print(f"4. Recirculation: cloak L_r={L_r_c:.0f}, A_r={A_r_c:.0f} "
f"vs uncontrolled L_r={L_r_unc:.0f}, A_r={A_r_unc:.0f}")
else:
print(f"4. Recirculation: cloak L_r={L_r_c:.0f}, A_r={A_r_c:.0f}")
# 5. Force statistics
cloak_forces = forces
if len(cloak_forces) > 0:
fx = cloak_forces[:, 0::2]
fy = cloak_forces[:, 1::2]
sigma_F = float(np.std(np.sum(fx, axis=1) + np.sum(fy, axis=1)))
mean_Fx = float(np.mean(fx))
print(f"5. Force stats: sigma_F={sigma_F:.6f}, mean_Fx={mean_Fx:.6f}")
# 6. Control amplitude
ppo_rollout_path = os.path.join(OUTPUT_DIR, "cloak", "ppo_rollout.npz")
if os.path.exists(ppo_rollout_path):
pr = np.load(ppo_rollout_path)
steady_action = pr.get("steady_action")
if steady_action is not None:
J_omega_rms = float(np.sum(np.abs(steady_action)))
print(f"6. J_omega_rms = {J_omega_rms:.4f} (norm action sum abs)")
# 7. eta_cloak_obs (control efficiency proxy)
if unc_fields is not None:
unc_sens = load_sensors("uncontrolled")
if unc_sens is not None:
unc_sensor_mean = np.mean(unc_sens["sensors"], axis=0)
E_unc = float(np.mean(np.abs(unc_sensor_mean - sensor_target_mean)))
E_cloak = E_sensor_mean
J_omega = J_omega_rms if 'J_omega_rms' in dir() else 1.0
eta_cloak_obs = (E_unc - E_cloak) / (J_omega + 1e-12)
print(f"7. eta_cloak_obs = {eta_cloak_obs:.4f} "
f"(E_unc={E_unc:.4f}, E_cloak={E_cloak:.4f})")
# Compile and save
steady_metrics = {
"E_mean_ux": E_mean_ux,
"E_mean_uy": E_mean_uy,
"E_mean_avg": E_mean,
"E_sensor_mean": E_sensor_mean,
"sensor_mean": sensor_mean.tolist(),
"eta_fluc": eta_fluc,
"L_r_cloak": L_r_c,
"A_r_cloak": A_r_c,
"sigma_F": sigma_F if 'sigma_F' in dir() else 0,
"mean_Fx": mean_Fx if 'mean_Fx' in dir() else 0,
"J_omega_rms": J_omega_rms if 'J_omega_rms' in dir() else 0,
"eta_cloak_obs": eta_cloak_obs if 'eta_cloak_obs' in dir() else 0,
"L_r_uncontrolled": L_r_unc if unc_fields is not None and 'L_r_unc' in dir() else None,
"A_r_uncontrolled": A_r_unc if unc_fields is not None and 'A_r_unc' in dir() else None,
}
out_dir = os.path.join(OUTPUT_DIR, "steady")
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, "steady_metrics.json"), "w") as f:
json.dump(steady_metrics, f, indent=2)
print(f"\nSaved to {out_dir}/steady_metrics.json")
print("Phase 5 complete.")
return 0
if __name__ == "__main__":
sys.exit(main())
src/CCD_analysis/scripts/utils.py
+308
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@@ -0,0 +1,308 @@
# CCD_analysis/scripts/utils.py
"""Shared utilities for CCD analysis pipeline.
All CFD uses LegacyCelerisLab (old API). Must run under conda pycuda_3_10.
"""
from __future__ import annotations
import json
import os
import sys
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
# Add project root for LegacyCelerisLab import
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from LegacyCelerisLab import FlowField # noqa: E402
from LegacyCelerisLab import utils as legacy_utils # noqa: E402
# ---------------------------------------------------------------------------
# Config loading
# ---------------------------------------------------------------------------
def load_configs(config_dir: str) -> Tuple[Any, Any]:
"""Load legacy (cuda_config, field_config) from config_dir."""
cuda_cfg = legacy_utils.load_cuda_config(
os.path.join(config_dir, "config_cuda.json")
)
field_cfg = legacy_utils.load_flow_field_config(
os.path.join(config_dir, "config_flowfield.json")
)
return cuda_cfg, field_cfg
# ---------------------------------------------------------------------------
# Field I/O from DDF (matches uni_test pattern)
# ---------------------------------------------------------------------------
def get_velocity_field(flow_field: FlowField, u0: float = 0.01) -> Tuple[np.ndarray, np.ndarray]:
"""Extract ux, uy fields from DDF on host. Returns (ux, uy) each (NY, NX)."""
flow_field.get_ddf()
NX = flow_field.FIELD_SHAPE[0]
NY = flow_field.FIELD_SHAPE[1]
ddf = flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf[:, :, 1] + ddf[:, :, 5] + ddf[:, :, 8]
- ddf[:, :, 3] - ddf[:, :, 6] - ddf[:, :, 7]) / u0
uy = (ddf[:, :, 2] + ddf[:, :, 5] + ddf[:, :, 6]
- ddf[:, :, 4] - ddf[:, :, 7] - ddf[:, :, 8]) / u0
return ux.astype(np.float32), uy.astype(np.float32)
def vorticity_from_fields(ux: np.ndarray, uy: np.ndarray) -> np.ndarray:
"""Compute z-vorticity omega = dv/dx - du/dy."""
return (np.gradient(uy, axis=1) - np.gradient(ux, axis=0)).astype(np.float64)
# ---------------------------------------------------------------------------
# Period detection helpers
# ---------------------------------------------------------------------------
def detect_dominant_frequency(
signal: np.ndarray, sample_dt: float
) -> Tuple[float, float, float]:
"""Detect dominant frequency via FFT.
Parameters
----------
signal : 1D array
Time series to analyse.
sample_dt : float
Time between samples (in same units as desired frequency).
Returns
-------
f_dom : float
Dominant frequency.
period : float
Corresponding period (1/f_dom).
peak_power : float
Power at dominant frequency.
"""
n = len(signal)
if n < 16:
return 0.0, 0.0, 0.0
y = signal - np.mean(signal)
window = np.hanning(n)
spec = np.abs(np.fft.rfft(y * window)) ** 2
freqs = np.fft.rfftfreq(n, d=sample_dt)
# Skip DC
idx = 1 + np.argmax(spec[1:])
f_dom = float(freqs[idx])
period = 1.0 / f_dom if f_dom > 0 else 0.0
return f_dom, period, float(spec[idx])
def detect_cycle_stability(
signal: np.ndarray, sample_dt: float
) -> Tuple[float, float, List[float]]:
"""Detect cycle lengths and compute stability metrics.
Parameters
----------
signal : 1D array
sample_dt : float
Returns
-------
cv_T : float
Coefficient of variation of detected cycle lengths.
mean_T : float
Mean cycle length in time units.
cycle_lengths : list of float
Detected cycle lengths.
"""
y = signal - np.mean(signal)
# Find rising zero-crossings
sign = np.sign(y)
crossings = np.where((sign[:-1] < 0) & (sign[1:] > 0))[0]
if len(crossings) < 2:
return 0.0, 0.0, []
cycle_lengths = np.diff(crossings).astype(float) * sample_dt
if len(cycle_lengths) < 2:
return 0.0, float(cycle_lengths[0]) if len(cycle_lengths) > 0 else 0.0, cycle_lengths.tolist()
mean_T = float(np.mean(cycle_lengths))
std_T = float(np.std(cycle_lengths))
cv_T = std_T / mean_T if mean_T > 0 else 0.0
return cv_T, mean_T, cycle_lengths.tolist()
# ---------------------------------------------------------------------------
# Phase resampling
# ---------------------------------------------------------------------------
def phase_resample(
data: np.ndarray,
cycle_starts: List[int],
n_pts: int = 24,
kind: str = "linear",
) -> np.ndarray:
"""Resample a multi-channel signal to uniform phase points per cycle.
Parameters
----------
data : (T, C) ndarray
Multi-channel time series.
cycle_starts : list of int
Indices where each cycle starts (rising zero-crossings).
n_pts : int
Number of phase points per cycle.
kind : str
Interpolation kind ('linear' or 'cubic').
Returns
-------
resampled : (n_cycles, n_pts, C) ndarray
"""
from scipy import interpolate
n_cycles = len(cycle_starts) - 1
if n_cycles < 1:
raise ValueError("Need at least 2 cycle starts")
C = data.shape[1] if data.ndim > 1 else 1
out = np.zeros((n_cycles, n_pts, C), dtype=np.float64)
for c in range(n_cycles):
i_start = cycle_starts[c]
i_end = cycle_starts[c + 1]
segment = data[i_start:i_end + 1]
seg_len = len(segment)
if seg_len < 2:
continue
old_phase = np.linspace(0, 2 * np.pi, seg_len)
new_phase = np.linspace(0, 2 * np.pi, n_pts, endpoint=False)
for ch in range(C):
interp = interpolate.interp1d(
old_phase, segment[:, ch] if segment.ndim > 1 else segment,
kind=kind, bounds_error=False, fill_value="extrapolate",
)
out[c, :, ch] = interp(new_phase)
return out
# ---------------------------------------------------------------------------
# POD
# ---------------------------------------------------------------------------
def compute_pod(snapshot_matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Compute POD from snapshot matrix.
Parameters
----------
snapshot_matrix : (n_points, n_snapshots) ndarray
Each column is one flattened snapshot.
Returns
-------
mean_field : (n_points,) ndarray
modes : (n_points, min(n_points, n_snapshots)) ndarray
Spatial modes (columns of U from SVD).
singular_values : (min_dim,) ndarray
coefficients : (min_dim, n_snapshots) ndarray
Temporal coefficients (Sigma V^T).
"""
mean_field = np.mean(snapshot_matrix, axis=1)
Q = snapshot_matrix - mean_field[:, None] # remove mean
U, s, Vt = np.linalg.svd(Q, full_matrices=False)
coeffs = (U.T @ Q) # alternative: np.diag(s) @ Vt
return mean_field, U, s, coeffs
def cumulative_energy(singular_values: np.ndarray) -> np.ndarray:
"""Return cumulative energy fraction."""
e = singular_values ** 2
return np.cumsum(e) / np.sum(e)
def e95_index(cumulative_energy: np.ndarray) -> int:
"""Return first index where cumulative energy >= 95%."""
return int(np.searchsorted(cumulative_energy, 0.95) + 1)
# ---------------------------------------------------------------------------
# CCD (reduced version, Lyu23-inspired)
# ---------------------------------------------------------------------------
def compute_reduced_ccd(
pod_coeffs: np.ndarray,
observable: np.ndarray,
Q_delay: int = 12,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Compute reduced CCD in POD coefficient space.
Parameters
----------
pod_coeffs : (r, N) ndarray
Standardized POD coefficients (r modes, N time steps).
observable : (m, N) ndarray
Standardized observable (m channels, N time steps).
Q_delay : int
Number of delay steps.
Returns
-------
W : (r, min(r, m*Q_delay)) ndarray
CCD directions in POD coefficient space.
sigma : (min_dim,) ndarray
Singular values (correlation strengths).
z : (min_dim, N) ndarray
CCD temporal coefficients.
"""
N = pod_coeffs.shape[1]
m = observable.shape[0]
# Build delay matrix P
# For each time t_i, p_i = [y(t_i+τ_1), ..., y(t_i+τ_Q)]
# where τ_j spans -Q_delay/2 to +Q_delay/2
half = Q_delay // 2
P_rows = []
for shift in range(-half, half + 1):
shifted = np.roll(observable, -shift, axis=1)
if shift < 0:
shifted[:, shift:] = 0 # zero pad edges
elif shift > 0:
shifted[:, :-shift] = 0
P_rows.append(shifted)
P = np.vstack(P_rows) # (m*Q_delay, N)
# CCD matrix: C = P * A^T / (N * sqrt(Q))
C = P @ pod_coeffs.T / (N * np.sqrt(Q_delay))
# SVD
R, s, Wt = np.linalg.svd(C, full_matrices=False)
W = Wt.T # (r, min_dim)
# CCD coefficients
z = W.T @ pod_coeffs # (min_dim, N)
return W, s, z
# ---------------------------------------------------------------------------
# Field stacking
# ---------------------------------------------------------------------------
def stack_velocity_fields(
ux_fields: List[np.ndarray],
uy_fields: List[np.ndarray],
) -> np.ndarray:
"""Stack list of (ux, uy) field pairs into snapshot matrix.
Each field is flattened, then ux and uy are interleaved.
Returns (2*nx*ny, N) matrix.
"""
snapshots = []
for ux, uy in zip(ux_fields, uy_fields):
q = np.concatenate([ux.ravel(), uy.ravel()])
snapshots.append(q)
return np.column_stack(snapshots)
src/CCD_analysis/scripts/validate_control.py
+561
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@@ -0,0 +1,561 @@
# CCD_analysis/scripts/validate_control.py
"""
WARNING: This validation script has NOT produced correct results.
Despite multiple iterations, the PPO inference does not reproduce thesis-level
reward/similarity values (cloak sim ~0.19 vs expected 0.90, illusion sim ~0.82 vs 0.98).
Known issues that need to be investigated:
1. The norm values seem reasonable but may not match training-time distributions
2. Obs layout (what obs[i] means) depends on object add order, which differs between
legacy_env_karman_cloak_standard.py and uni_test.ipynb -- need to determine which
was actually used during training
3. The FIFO initialization may not exactly match training-time behavior
4. Action smoothing (legacy FlowField.run() uses exponential smoothing weight=0.1)
may affect dynamics in ways not accounted for
DO NOT USE these results for analysis. Fix the PPO replay first.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
_ANALYSIS = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if _ANALYSIS not in sys.path:
sys.path.insert(0, _ANALYSIS)
from LegacyCelerisLab import FlowField
from scripts.cfg import CONFIG_DIR, OUTPUT_DIR, L0, NX, NY, CENTER_Y
from scripts.utils import load_configs, get_velocity_field
# -- Constants --
FIFO_LEN = 150
DATA_TYPE = np.float32
U0 = 0.01
U0_ILLUSION = 0.02
SAMPLE_INTERVAL = 800
SAMPLE_INTERVAL_ILL = 600
MODEL_CLOAK = os.path.join(_REPO, "models", "old", "d1a3o12_re100.zip")
MODEL_ILLUSION = os.path.join(_REPO, "models", "250525", "d1a3o14_250525_imit_1L_2U_600S.zip")
# Geometry
PR = L0 / 2.0 # pinball radius = 10
SR = L0 / 4.0 # sensor radius = 5
# Cloak layout (lattice units)
DIST_POS = (10.0 * L0, CENTER_Y, 0.0)
SENSOR_X = 40.0 * L0
SENSOR_YS = [CENTER_Y + 2.0 * L0, CENTER_Y, CENTER_Y - 2.0 * L0]
FRONT_POS = (30.0 * L0, CENTER_Y, 0.0)
BOTTOM_POS = (31.3 * L0, CENTER_Y - 0.75 * L0, 0.0)
TOP_POS = (31.3 * L0, CENTER_Y + 0.75 * L0, 0.0)
# Illusion layout
ILL_SENSOR_X = 30.0 * L0
ILL_SENSOR_YS = [CENTER_Y + 2.0 * L0, CENTER_Y, CENTER_Y - 2.0 * L0]
ILL_FRONT = (19.0 * L0, CENTER_Y, 0.0)
ILL_BOTTOM = (20.3 * L0, CENTER_Y + 0.75 * L0, 0.0)
ILL_TOP = (20.3 * L0, CENTER_Y - 0.75 * L0, 0.0)
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _calc_lag(t, s):
tm = float(np.mean(t))
sm = float(np.mean(s))
corr = np.correlate(t - tm, s - sm, mode="full")
lags = np.arange(-len(t) + 1, len(t))
return int(lags[np.argmax(corr)])
def _calc_dtw_sim(t, s):
n, m = len(t), len(s)
dtw = np.full((n + 1, m + 1), np.inf)
dtw[0, 0] = 0.0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(float(t[i - 1]) - float(s[j - 1]))
dtw[i, j] = cost + min(dtw[i - 1, j], dtw[i, j - 1], dtw[i - 1, j - 1])
return float(1.0 - dtw[n, m] / n)
def _save_vorticity_png(ff, path, title="", u0=U0):
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
ux, uy = get_velocity_field(ff, u0=u0)
omega = np.gradient(uy, axis=1) - np.gradient(ux, axis=0) # dv/dx - du/dy
abs_o = np.abs(omega[np.isfinite(omega)])
vmax = float(np.percentile(abs_o, 99.5)) if abs_o.size > 0 else 1.0
if vmax <= 0:
vmax = 1.0
ny, nx = omega.shape
fig, ax = plt.subplots(figsize=(min(18, max(8, nx / 60)), min(10, max(3, ny / 40))))
im = ax.imshow(omega, origin="lower", aspect="equal", cmap="RdBu_r",
vmin=-vmax, vmax=vmax, extent=(0, nx - 1, 0, ny - 1))
ax.set_xlabel("x (lattice)")
ax.set_ylabel("y (lattice)")
if title:
ax.set_title(title)
fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04, label=r"$\omega_z$")
fig.tight_layout()
fig.savefig(path, dpi=150, bbox_inches="tight")
plt.close(fig)
def _load_ppo(model_path, device, s_dim=12, a_dim=3):
import torch
from torch.nn import Module
from stable_baselines3 import PPO
import gymnasium as gym
from gymnasium import spaces
class Sin(Module):
def forward(self, x):
return torch.sin(x)
class DummyEnv(gym.Env):
def __init__(self):
super().__init__()
self.observation_space = spaces.Box(low=-1, high=1, shape=(s_dim,), dtype=np.float32)
self.action_space = spaces.Box(low=-1, high=1, shape=(a_dim,), dtype=np.float32)
def reset(self, seed=None):
return np.zeros(s_dim, dtype=np.float32), {}
def step(self, action):
return np.zeros(s_dim, dtype=np.float32), 0.0, False, False, {}
def render(self):
pass
return PPO.load(model_path, env=DummyEnv(), device=device)
def _analyze_harmonics(states, n=5):
N, D = states.shape
r = []
for d in range(D):
y = states[:, d]
fc = np.fft.rfft(y)
fr = np.fft.rfftfreq(N, d=1)
amps = 2.0 * np.abs(fc) / N
ph = np.angle(fc)
idx = np.argsort(amps[1:])[::-1][:n] + 1
r.append({'dc': float(np.real(fc[0]) / N), 'amps': amps[idx].tolist(),
'freqs': fr[idx].tolist(), 'phases': ph[idx].tolist()})
return r
def _gen_target(t, harm):
t = np.asarray(t)
D = len(harm)
r = np.zeros((t.size, D), dtype=np.float32)
for d, h in enumerate(harm):
v = np.full(t.shape, h['dc'], dtype=np.float32)
for a, f, p in zip(h['amps'], h['freqs'], h['phases']):
v += a * np.cos(2 * np.pi * f * t + p)
r[:, d] = v
if r.shape[0] == 1:
return r[0]
return r
# ---------------------------------------------------------------------------
# Cloak validation (EXACTLY matching analysis_crossre + legacy_env)
# ---------------------------------------------------------------------------
def validate_cloak(device_id, out_dir):
print("=" * 60)
print("Validating Cloak (Karman)")
print("=" * 60)
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=0.004, velocity=U0)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
# -- Phase 1: target recording (dist_cyl + 3 sensors) --
print("\n--- Target recording ---")
ff.add_cylinder(DIST_POS, L0) # dist(0)
for y in SENSOR_YS:
ff.add_sensor((SENSOR_X, y, 0.0), SR) # sensors(1,2,3)
n_obj = ff.obs.size // 2 # 4
ff.run(int(4 * NX / U0), np.zeros(n_obj, dtype=DATA_TYPE))
target_states = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj, dtype=DATA_TYPE))
# obs layout: dist(0) + 3 sensors(1,2,3) → 4×2=8 values
# obs[2:8] = s0_ux,uy, s1_ux,uy, s2_ux,uy = 6 sensors
target_states = np.vstack((target_states, ff.obs.copy()[2:8]))
print(f" target: {target_states.shape}")
# -- Phase 2: add pinball (ids 4,5,6) --
print("\n--- Add pinball ---")
ff.add_cylinder(FRONT_POS, PR) # front(4)
ff.add_cylinder(BOTTOM_POS, PR) # bottom(5)
ff.add_cylinder(TOP_POS, PR) # top(6)
n_obj = ff.obs.size // 2 # 7
ff.run(int(4 * NX / U0), np.zeros(n_obj, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf() # checkpoint
# -- Phase 3: Norm computation --
print("\n--- Norm (obs[2:14]) ---")
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[2:14]) # [sensors(6), forces(6)]
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_deviation = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_deviation[i])))
print(f" force_norm_fact={force_norm_fact:.6f}")
# -- Phase 4: Bias-action FIFO (uni_test: [0,0,0,0,0,-4*U0,4*U0]) --
print("\n--- Bias FIFO ---")
ff.apply_ddf()
bias = np.zeros(n_obj, dtype=DATA_TYPE)
bias[5] = -4.0 * U0 # bottom
bias[6] = 4.0 * U0 # top
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias)
fifo.append(ff.obs.copy()[2:14])
save_states = list(fifo)
ff.apply_ddf()
# -- Phase 5: PPO inference --
print("\n--- PPO inference (500 steps) ---")
import torch
dev = f"cuda:{device_id}" if torch.cuda.is_available() else "cpu"
model = _load_ppo(MODEL_CLOAK, device=dev, s_dim=12, a_dim=3)
model.set_random_seed(0)
n_steps = 500
fifo = deque(maxlen=FIFO_LEN)
for s in save_states:
fifo.append(np.array(s, dtype=DATA_TYPE))
obs = np.zeros(12, dtype=DATA_TYPE)
rewards, sims, cds, cls = [], [], [], []
for step in range(n_steps):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(DATA_TYPE).flatten()
# Action: legacy pattern, pinball at indices 4,5,6
temp_a = np.zeros(n_obj, dtype=DATA_TYPE)
temp_a[4:7] = (action * 8.0 + np.array([0.0, -4.0, 4.0], dtype=DATA_TYPE)) * U0
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, temp_a)
finally:
ff.context.pop()
obs_slice = ff.obs.copy()[2:14] # [sensors(6), forces(6)]
fifo.append(obs_slice)
# Observation: [forces_norm(6), sens_norm(6)] ← matches analysis_crossre order
forces_norm = obs_slice[6:12] / force_norm_fact
sens_norm = (obs_slice[0:6] - sens_deviation) / sens_norm_fact
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(DATA_TYPE)
# Reward (legacy env style: cd/cl from forces in obs_slice[6:12])
sarr = np.array(fifo, dtype=DATA_TYPE)
if len(sarr) >= 30:
f = sarr[-1, 6:12] / force_norm_fact
cd = float((f[0] + f[2] + f[4]) / 3.0)
cl = float((f[1] + f[3] + f[5]) / 3.0)
# DTW: lag from middle sensor (index 1 in sensor block = obs[4] in obs[2:14] = sensor1_uy)
ref = target_states[30:60, 1]
cur = sarr[-30:, 1]
lag = _calc_lag(ref, cur)
sim = 0.0
for i in range(6):
t_seq = np.roll(target_states[:, i], -lag)[30:60]
s_seq = sarr[-30:, i]
sim += _calc_dtw_sim(t_seq, s_seq) / 6.0
r_cd = float(np.exp(-abs(cd * 20.0)))
r_cl = float(np.exp(-abs(cl * 80.0)))
r_sim = float(np.exp(-10.0 * abs(sim - 1.0)))
reward = float(min(0.3 * r_cd + 0.4 * r_cl + 0.3 * r_sim, 1.0))
else:
cd, cl, sim, reward = 0.0, 0.0, 0.0, 0.0
rewards.append(reward)
sims.append(sim)
cds.append(cd)
cls.append(cl)
_save_vorticity_png(ff, os.path.join(out_dir, "cloak_vorticity_final.png"),
title="Cloak (Karman) Re=100", u0=U0)
tail = 100
result = {
"case": "cloak_karman", "n_steps": n_steps,
"mean_reward_last100": float(np.mean(rewards[-tail:])),
"std_reward_last100": float(np.std(rewards[-tail:])),
"mean_similarity_last100": float(np.mean(sims[-tail:])),
"mean_cd_last100": float(np.mean(cds[-tail:])),
"mean_cl_last100": float(np.mean(cls[-tail:])),
"force_norm_fact": force_norm_fact,
"vorticity_png": "cloak_vorticity_final.png",
}
print(f"\n mean_reward={result['mean_reward_last100']:.4f} "
f"sim={result['mean_similarity_last100']:.4f} cd={result['mean_cd_last100']:.4f}")
del ff, model
return result
# ---------------------------------------------------------------------------
# Illusion validation
# ---------------------------------------------------------------------------
def validate_illusion(device_id, out_dir):
print("=" * 60)
print("Validating Illusion (1L)")
print("=" * 60)
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=0.008, velocity=U0_ILLUSION)
# -- Target recording --
print("\n--- Target recording ---")
ff_tgt = FlowField(field_cfg, cuda_cfg, device_id=device_id)
ff_tgt.add_cylinder((20.0 * L0, CENTER_Y, 0.0), L0) # target cylinder
for y in ILL_SENSOR_YS:
ff_tgt.add_sensor((ILL_SENSOR_X, y, 0.0), SR)
n_tgt = ff_tgt.obs.size // 2 # 4
ff_tgt.run(int(4 * NX / U0_ILLUSION), np.zeros(n_tgt, dtype=DATA_TYPE))
target_states = np.empty((0, 8), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff_tgt.run(SAMPLE_INTERVAL_ILL, np.zeros(n_tgt, dtype=DATA_TYPE))
target_states = np.vstack((target_states, ff_tgt.obs.copy()[0:8]))
harmonics = _analyze_harmonics(target_states, 5)
print(f" target: {target_states.shape}, harmonics: {len(harmonics)} ch")
del ff_tgt
# -- Pinball env (6 objects: 3 sensors + 3 pinball) --
# Add sensors first, then pinball (matches legacy_env_imit.py)
print("\n--- Build pinball env ---")
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
for y in ILL_SENSOR_YS:
ff.add_sensor((ILL_SENSOR_X, y, 0.0), SR) # sensors(0,1,2)
ff.add_cylinder(ILL_FRONT, PR) # front(3)
ff.add_cylinder(ILL_BOTTOM, PR) # bottom(4)
ff.add_cylinder(ILL_TOP, PR) # top(5)
n_obj = ff.obs.size // 2 # 6
ff.run(int(4 * NX / U0_ILLUSION), np.zeros(n_obj, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf()
# -- Norm (obs[0:12] = [sensors(6), forces(6)]) --
print("\n--- Norm ---")
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL_ILL, np.zeros(n_obj, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_deviation = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_deviation[i])))
print(f" force_norm_fact={force_norm_fact:.6f}")
# -- Bias FIFO --
print("\n--- Bias FIFO ---")
ff.apply_ddf()
bias = np.zeros(n_obj, dtype=DATA_TYPE)
bias[4] = -1.0 * U0_ILLUSION
bias[5] = 1.0 * U0_ILLUSION
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL_ILL, bias)
fifo.append(ff.obs.copy()[0:12])
save_states = list(fifo)
ff.apply_ddf()
# -- PPO inference --
print("\n--- PPO inference (500 steps) ---")
import torch
dev = f"cuda:{device_id}" if torch.cuda.is_available() else "cpu"
model = _load_ppo(MODEL_ILLUSION, device=dev, s_dim=14, a_dim=3)
model.set_random_seed(19)
n_steps = 500
fifo = deque(maxlen=FIFO_LEN)
for s in save_states:
fifo.append(np.array(s, dtype=DATA_TYPE))
obs = np.zeros(14, dtype=DATA_TYPE)
rewards, sims, cds, cls = [], [], [], []
for step in range(n_steps):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(DATA_TYPE).flatten()
temp_a = np.zeros(n_obj, dtype=DATA_TYPE)
temp_a[3:6] = (action * 8.0 + np.array([0.0, -2.0, 2.0], dtype=DATA_TYPE)) * U0_ILLUSION
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL_ILL, temp_a)
finally:
ff.context.pop()
obs_slice = ff.obs.copy()[0:12]
fifo.append(obs_slice)
# 14-dim obs: [forces_norm(6), sens_norm(6), target_cd, target_cl]
forces_norm = obs_slice[6:12] / force_norm_fact
sens_norm = (obs_slice[0:6] - sens_deviation) / sens_norm_fact
target_recon = _gen_target(step, harmonics)
t_cd_n = float(target_recon[0]) / force_norm_fact
t_cl_n = float(target_recon[1]) / force_norm_fact
obs = np.clip(np.hstack([forces_norm, sens_norm, t_cd_n, t_cl_n]),
-1.0, 1.0).astype(DATA_TYPE)
# Reward
sarr = np.array(fifo, dtype=DATA_TYPE)
if len(sarr) >= 36:
f = sarr[-1, 6:12] / force_norm_fact
cd = float(f[0] + f[2] + f[4]) # SUM
cl = float(f[1] + f[3] + f[5])
ref = target_states[36:72, 3]
cur = sarr[-36:, 3]
lag = _calc_lag(ref, cur)
sim = 0.0
for i in range(6):
t_seq = np.roll(target_states[:, i + 2], -lag)[36:72]
s_seq = sarr[-36:, i]
sim += _calc_dtw_sim(t_seq, s_seq) / 6.0
t_cd = float(target_recon[0]) / force_norm_fact
t_cl = float(target_recon[1]) / force_norm_fact
r_cd = float(np.exp(-abs((cd - t_cd) * 10.0)))
r_cl = float(np.exp(-abs((cl - t_cl) * 10.0)))
r_sim = float(np.exp(-10.0 * abs(sim - 1.0)))
reward = float(min(0.3 * r_cd + 0.3 * r_cl + 0.4 * r_sim, 1.0))
else:
cd, cl, sim, reward = 0.0, 0.0, 0.0, 0.0
rewards.append(reward)
sims.append(sim)
cds.append(cd)
cls.append(cl)
_save_vorticity_png(ff, os.path.join(out_dir, "illusion_vorticity_final.png"),
title="Illusion (1L) Re=100", u0=U0_ILLUSION)
tail = 100
result = {
"case": "illusion_1L", "n_steps": n_steps,
"mean_reward_last100": float(np.mean(rewards[-tail:])),
"std_reward_last100": float(np.std(rewards[-tail:])),
"mean_similarity_last100": float(np.mean(sims[-tail:])),
"mean_cd_last100": float(np.mean(cds[-tail:])),
"mean_cl_last100": float(np.mean(cls[-tail:])),
"force_norm_fact": force_norm_fact,
"vorticity_png": "illusion_vorticity_final.png",
}
print(f"\n mean_reward={result['mean_reward_last100']:.4f} "
f"sim={result['mean_similarity_last100']:.4f} cd={result['mean_cd_last100']:.4f}")
del ff, model
return result
# ---------------------------------------------------------------------------
# Baseline
# ---------------------------------------------------------------------------
def validate_uncontrolled(device_id, out_dir):
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=0.004, velocity=U0)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
for y in SENSOR_YS:
ff.add_sensor((SENSOR_X, y, 0.0), SR)
ff.add_cylinder(FRONT_POS, PR)
ff.add_cylinder(BOTTOM_POS, PR)
ff.add_cylinder(TOP_POS, PR)
ff.run(int(4 * NX / U0), np.zeros(6, dtype=DATA_TYPE))
for _ in range(200):
ff.run(SAMPLE_INTERVAL, np.zeros(6, dtype=DATA_TYPE))
_save_vorticity_png(ff, os.path.join(out_dir, "uncontrolled_vorticity_final.png"),
title="Uncontrolled Pinball Re=100", u0=U0)
del ff
return {"case": "uncontrolled", "vorticity_png": "uncontrolled_vorticity_final.png"}
def validate_target(device_id, out_dir):
cuda_cfg, field_cfg = load_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=0.004, velocity=U0)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
ff.add_cylinder((20.0 * L0, CENTER_Y, 0.0), L0)
for y in SENSOR_YS:
ff.add_sensor((SENSOR_X, y, 0.0), SR)
ff.run(int(4 * NX / U0), np.zeros(4, dtype=DATA_TYPE))
for _ in range(200):
ff.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE))
_save_vorticity_png(ff, os.path.join(out_dir, "target_vorticity_final.png"),
title="Target 2D Cylinder Re=100", u0=U0)
del ff
return {"case": "target_cylinder", "vorticity_png": "target_vorticity_final.png"}
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
ap = argparse.ArgumentParser(description="Validate control quality")
ap.add_argument("--device", type=int, default=2)
ap.add_argument("--case", type=str, required=True,
choices=["all", "cloak", "illusion", "uncontrolled", "target"])
args = ap.parse_args()
out_dir = os.path.join(OUTPUT_DIR, "validation")
os.makedirs(out_dir, exist_ok=True)
t0 = time.time()
results = {}
if args.case in ("all", "cloak"):
results["cloak"] = validate_cloak(args.device, out_dir)
if args.case in ("all", "illusion"):
results["illusion"] = validate_illusion(args.device, out_dir)
if args.case in ("all", "uncontrolled"):
results["uncontrolled"] = validate_uncontrolled(args.device, out_dir)
if args.case in ("all", "target"):
results["target"] = validate_target(args.device, out_dir)
with open(os.path.join(out_dir, "validation_results.json"), "w") as f:
json.dump({"device": args.device, "elapsed_sec": time.time() - t0,
"results": results}, f, indent=2)
print(f"\n{'='*60}\nSummary\n{'='*60}")
for c, r in results.items():
if "mean_reward_last100" in r:
print(f" {c}: reward={r['mean_reward_last100']:.4f} sim={r['mean_similarity_last100']:.4f}")
else:
print(f" {c}: baseline")
print(f"\nVorticity: {out_dir}/ | Total: {time.time()-t0:.1f}s")
return 0
if __name__ == "__main__":
main()
src/__init__.py
-10
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@@ -1,11 +1 @@
"""
DynamisLab: Machine Learning for Computational Fluid Dynamics
"""
__version__ = '0.1.0'
__author__ = 'Frank14f'
from . import config
from . import environments
__all__ = ['config', 'environments', '__version__']
src/analysis_cloak/__init__.py
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src/analysis_cloak/common/__init__.py
View File
src/analysis_cloak/common/feature_builder.py
+214
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"""Unified feature builder for all cloak scenes.
Produces dimensionless features with consistent G-equivariant structure.
All scenes (Karman, steady, vortex, erase) use this same builder.
"""
from __future__ import annotations
from typing import Dict, List, Optional, Tuple
import numpy as np
# -- Physical constants ------------------------------------------------------
U0 = 0.01 # inlet velocity (lattice units)
D_CYL = 20.0 # cylinder diameter (lattice)
# -- Dimensionless conversion ------------------------------------------------
def compute_dimensionless(
sensors: np.ndarray, # (T, 6) raw lattice [s0_ux,s0_uy, s1_ux,s1_uy, s2_ux,s2_uy]
forces: np.ndarray, # (T, 6) raw lattice [f0_fx,f0_fy, f1_fx,f1_fy, f2_fx,f2_fy]
u0: float = U0,
d: float = D_CYL,
rho: float = 1.0,
) -> Dict[str, np.ndarray]:
"""Convert raw lattice CFD data to dimensionless quantities.
Sensor order: [s0_ux,s0_uy, s1_ux,s1_uy, s2_ux,s2_uy]
where s0=top(y=+2L0), s1=mid(y=0), s2=bottom(y=-2L0)
Force order: [front_fx,front_fy, bottom_fx,bottom_fy, top_fx,top_fy]
Returns:
u_hat_B, u_hat_C, u_hat_T: nondim streamwise velocity (bottom/centre/top)
v_hat_B, v_hat_C, v_hat_T: nondim crosswise velocity
Cd_F, Cd_T, Cd_B: drag coefficient per cylinder
Cl_F, Cl_T, Cl_B: lift coefficient per cylinder
"""
s = np.asarray(sensors, dtype=np.float64)
f = np.asarray(forces, dtype=np.float64)
# Sensor positions: s0=top, s1=centre, s2=bottom
# Convention: B=bottom=s2, C=centre=s1, T=top=s0
return {
"u_hat_T": s[:, 0] / u0,
"v_hat_T": s[:, 1] / u0,
"u_hat_C": s[:, 2] / u0,
"v_hat_C": s[:, 3] / u0,
"u_hat_B": s[:, 4] / u0,
"v_hat_B": s[:, 5] / u0,
"Cd_F": 2.0 * f[:, 0] / (rho * u0**2 * d),
"Cl_F": 2.0 * f[:, 1] / (rho * u0**2 * d),
"Cd_B": 2.0 * f[:, 2] / (rho * u0**2 * d),
"Cl_B": 2.0 * f[:, 3] / (rho * u0**2 * d),
"Cd_T": 2.0 * f[:, 4] / (rho * u0**2 * d),
"Cl_T": 2.0 * f[:, 5] / (rho * u0**2 * d),
}
# -- G operator (corrected) --------------------------------------------------
def apply_G_alpha(alpha: np.ndarray) -> np.ndarray:
"""Apply mirror G to action: [aF, aT, aB] -> [-aF, -aB, -aT]."""
return np.array([-alpha[0], -alpha[2], -alpha[1]], dtype=alpha.dtype)
def apply_G_x(dim: Dict[str, np.ndarray],
a_prev: np.ndarray,
a_prev2: np.ndarray) -> Tuple[Dict, np.ndarray, np.ndarray]:
"""Apply G to dimensionless state.
Returns (G_dim, G_a_prev, G_a_prev2) with corrected sign rules.
"""
G_dim = {
"u_hat_B": dim["u_hat_T"], "u_hat_C": dim["u_hat_C"], "u_hat_T": dim["u_hat_B"],
"v_hat_B": -dim["v_hat_T"], "v_hat_C": -dim["v_hat_C"], "v_hat_T": -dim["v_hat_B"],
"Cd_F": dim["Cd_F"], "Cd_T": dim["Cd_B"], "Cd_B": dim["Cd_T"],
"Cl_F": -dim["Cl_F"], "Cl_T": -dim["Cl_B"], "Cl_B": -dim["Cl_T"],
}
G_a_prev = np.column_stack([-a_prev[:, 0], -a_prev[:, 2], -a_prev[:, 1]])
G_a_prev2 = np.column_stack([-a_prev2[:, 0], -a_prev2[:, 2], -a_prev2[:, 1]])
return G_dim, G_a_prev, G_a_prev2
# -- Feature key definitions -------------------------------------------------
CORE_FEAT_KEYS = [
"u_m", "u_a", "u_c",
"v_a",
"Cd_tot", "Cd_rear",
"Cl_tot", "Cl_diff",
"sin_ua", "cos_ua",
"aF_lag1", "aB_lag1", "aT_lag1",
"daF", "daB", "daT",
]
MU_FEAT_KEYS = ["mu", "mu_u_a", "mu_v_a", "mu_Cd_tot", "mu_Cl_diff"]
ALL_FEAT_KEYS = CORE_FEAT_KEYS + MU_FEAT_KEYS
# -- Feature computation -----------------------------------------------------
def compute_features(
dim: Dict[str, np.ndarray],
actions_prev: np.ndarray, # (T, 3) physical omega(t-1) or nondim alpha(t-1)
actions_prev2: np.ndarray, # (T, 3) physical omega(t-2)
mu: float,
alpha_mode: bool = False, # if True, actions_prev are already nondim alpha
include_mu: bool = True,
) -> Dict[str, np.ndarray]:
"""Compute unified feature dictionary from dimensionless primitives.
Args:
dim: from compute_dimensionless()
actions_prev: lagged actions (physical omega or nondim alpha)
actions_prev2: twice-lagged actions
mu: 1/Re_D
alpha_mode: if True, actions are already nondim; else convert
include_mu: include mu modulation terms
Returns dict with all features as (T,) or (T,3) arrays.
"""
T = actions_prev.shape[0]
u_B, u_C, u_T = dim["u_hat_B"], dim["u_hat_C"], dim["u_hat_T"]
v_B, v_C, v_T = dim["v_hat_B"], dim["v_hat_C"], dim["v_hat_T"]
Cd_F, Cd_T, Cd_B = dim["Cd_F"], dim["Cd_T"], dim["Cd_B"]
Cl_F, Cl_T, Cl_B = dim["Cl_F"], dim["Cl_T"], dim["Cl_B"]
# If actions are in physical omega, convert to nondim alpha
if alpha_mode:
a = actions_prev.astype(np.float64)
a2 = actions_prev2.astype(np.float64)
else:
a = actions_prev.astype(np.float64) / U0
a2 = actions_prev2.astype(np.float64) / U0
sym = {}
# Sensor combinations (nondim)
sym["u_m"] = (u_B + u_C + u_T) / 3.0
sym["u_a"] = (u_T - u_B) / 2.0
sym["u_c"] = u_C.copy()
sym["v_a"] = (v_T - v_B) / 2.0
# Force combinations (dimensionless Cd/Cl)
sym["Cd_tot"] = Cd_F + Cd_T + Cd_B
sym["Cd_rear"] = Cd_T + Cd_B
sym["Cl_tot"] = Cl_F + Cl_T + Cl_B
sym["Cl_diff"] = Cl_T - Cl_B
# Phase
sym["sin_ua"] = np.sin(np.pi * sym["u_a"])
sym["cos_ua"] = np.cos(np.pi * sym["u_a"])
# Memory (nondim alpha)
sym["aF_lag1"] = a[:, 0]
sym["aB_lag1"] = a[:, 1]
sym["aT_lag1"] = a[:, 2]
sym["daF"] = a[:, 0] - a2[:, 0]
sym["daB"] = a[:, 1] - a2[:, 1]
sym["daT"] = a[:, 2] - a2[:, 2]
# Mu modulation
if include_mu:
sym["mu"] = np.full(T, mu, dtype=np.float64)
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
sym["mu_Cd_tot"] = sym["Cd_tot"] * mu
sym["mu_Cl_diff"] = sym["Cl_diff"] * mu
return sym
def build_feature_matrix(
sym: Dict[str, np.ndarray],
feat_keys: List[str],
add_bias: bool = True,
) -> np.ndarray:
"""Build feature matrix (T, N) from symbol dict."""
cols = []
if add_bias:
cols.append(np.ones(sym[feat_keys[0]].shape[0], dtype=np.float64))
for k in feat_keys:
if k in sym:
cols.append(np.asarray(sym[k], dtype=np.float64))
else:
# Missing key (e.g. mu terms when include_mu=False) -> zero
T = sym.get("u_m", np.ones(1)).shape[0]
cols.append(np.zeros(T, dtype=np.float64))
return np.column_stack(cols)
def get_feature_names(feat_keys: List[str], add_bias: bool = True) -> List[str]:
"""Get feature names matching build_feature_matrix output."""
names = []
if add_bias:
names.append("bias")
names.extend(feat_keys)
return names
# -- Scene metadata ----------------------------------------------------------
def get_scene_metadata(scene: str) -> dict:
"""Return default metadata for a cloak scene."""
meta = {
"karman": {"scene_id": "karman", "control_interval": 800, "target_type": "periodic"},
"steady": {"scene_id": "steady", "control_interval": 800, "target_type": "steady"},
"vortex_lamb": {"scene_id": "vortex_lamb", "control_interval": 800, "target_type": "transient"},
"vortex_taylor": {"scene_id": "vortex_taylor", "control_interval": 800, "target_type": "transient"},
"erase": {"scene_id": "erase", "control_interval": 600, "target_type": "periodic"},
}
return meta.get(scene, {"scene_id": scene, "control_interval": 800, "target_type": "unknown"})
src/analysis_cloak/common/prebuild_features.py
+81
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"""Pre-build feature matrices for SR (run in pycuda_3_10).
Saves pickle files that run_sr_gplearn.py can load in sr_env.
Usage:
conda run -n pycuda_3_10 python prebuild_features.py
"""
from __future__ import annotations
import os
import pickle
import sys
import numpy as np
_THIS = os.path.abspath(os.path.dirname(__file__))
_PARENT = os.path.abspath(os.path.join(_THIS, "..")) # src/analysis_cloak/
_REPO = os.path.abspath(os.path.join(_THIS, "..", "..", ".."))
for p in [_PARENT, os.path.join(_PARENT, "..", "analysis_crossre", "scripts"), _REPO]:
if p not in sys.path:
sys.path.insert(0, p)
from common.feature_builder import (
compute_dimensionless, compute_features, build_feature_matrix,
get_feature_names, ALL_FEAT_KEYS, U0,
)
from utils import action_to_physical
from cfg import OUTPUT_DIR, ACTION_SCALE, ACTION_BIAS
def build(scene: str):
X_f_l, X_r_l, y_l = [], [], []
if scene == "karman":
for rc in [50, 100, 200]:
npz = np.load(os.path.join(OUTPUT_DIR, f"re{rc}", "controlled.npz"))
s, f = npz["sensors"].astype(np.float64), npz["forces"].astype(np.float64)
ap = action_to_physical(npz["actions"].astype(np.float64),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0)
mu = 2.0 / rc
T = s.shape[0]
a1 = np.zeros((T, 3), dtype=np.float64)
a2 = np.zeros((T, 3), dtype=np.float64)
a1[1:] = ap[:-1]; a2[2:] = ap[:-2]
dim = compute_dimensionless(s, f, u0=U0, d=20.0)
sym = compute_features(dim, a1, a2, mu, alpha_mode=True, include_mu=True)
X_f_l.append(build_feature_matrix(sym, ALL_FEAT_KEYS, add_bias=False)[2:])
X_r_l.append(build_feature_matrix(sym, ALL_FEAT_KEYS, add_bias=True)[2:])
y_l.append(ap[2:])
else:
npz = np.load(os.path.join(_REPO, "src", "analysis_cloak", "scenes",
"steady", "closed_loop", "steady_data.npz"))
s, f = npz["sensors"].astype(np.float64), npz["forces"].astype(np.float64)
ap = npz["actions"].astype(np.float64)
mu = 2.0 / 100
T = s.shape[0]
a1 = np.zeros((T, 3), dtype=np.float64)
a2 = np.zeros((T, 3), dtype=np.float64)
a1[1:] = ap[:-1]; a2[2:] = ap[:-2]
dim = compute_dimensionless(s, f, u0=U0, d=20.0)
sym = compute_features(dim, a1, a2, mu, alpha_mode=True, include_mu=True)
X_f_l.append(build_feature_matrix(sym, ALL_FEAT_KEYS, add_bias=False)[2:])
X_r_l.append(build_feature_matrix(sym, ALL_FEAT_KEYS, add_bias=True)[2:])
y_l.append(ap[2:])
data = {
"X_f": np.vstack(X_f_l),
"X_r": np.vstack(X_r_l),
"y": np.vstack(y_l),
"fn_f": get_feature_names(ALL_FEAT_KEYS, add_bias=False),
"fn_r": get_feature_names(ALL_FEAT_KEYS, add_bias=True),
}
out = os.path.join(_REPO, "src", "analysis_cloak", "scenes", scene, "sr", f"{scene}_features.pkl")
os.makedirs(os.path.dirname(out), exist_ok=True)
with open(out, "wb") as f:
pickle.dump(data, f)
print(f"Saved {scene} features: X_f={data['X_f'].shape}, X_r={data['X_r'].shape}")
if __name__ == "__main__":
for s in ["karman", "steady"]:
build(s)
src/analysis_cloak/common/run_sindy.py
+253
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@@ -0,0 +1,253 @@
"""Unified SINDy fitting for all cloak scenes.
Usage:
conda run -n pycuda_3_10 python run_sindy.py --scene karman
conda run -n pycuda_3_10 python run_sindy.py --scene steady
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import Dict, List, Tuple
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from src.analysis_cloak.common.feature_builder import (
compute_dimensionless,
compute_features,
build_feature_matrix,
get_feature_names,
apply_G_x,
CORE_FEAT_KEYS,
MU_FEAT_KEYS,
ALL_FEAT_KEYS,
U0,
get_scene_metadata,
)
from src.analysis_crossre.scripts.cfg import OUTPUT_DIR as CROSSRE_OUTPUT
from src.analysis_crossre.scripts.utils import action_to_physical, fit_channel
from src.analysis_crossre.scripts.cfg import ACTION_SCALE, ACTION_BIAS
THRESHOLDS = [0.0, 0.001, 0.002, 0.005, 0.01, 0.015, 0.02, 0.03, 0.05, 0.1]
def load_karman_data(re_code: int) -> Tuple:
"""Load Karman cloak data for a single Re."""
case_dir = os.path.join(CROSSRE_OUTPUT, f"re{re_code}")
npz_path = os.path.join(case_dir, "controlled.npz")
if not os.path.isfile(npz_path):
raise FileNotFoundError(f"Missing {npz_path}")
data = np.load(npz_path)
sensors = data["sensors"].astype(np.float64)
forces = data["forces"].astype(np.float64)
actions_norm = data["actions"].astype(np.float64)
actions_phys = action_to_physical(
actions_norm, scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0)
mu = 2.0 / re_code
return sensors, forces, actions_phys, mu
def load_steady_data():
"""Load steady cloak data."""
steady_dir = os.path.join(_REPO, "src", "analysis_cloak", "scenes", "steady", "closed_loop")
npz_path = os.path.join(steady_dir, "steady_data.npz")
if not os.path.isfile(npz_path):
raise FileNotFoundError(f"Missing {npz_path}. Run gen_steady_data.py first.")
data = np.load(npz_path)
sensors = data["sensors"].astype(np.float64)
forces = data["forces"].astype(np.float64)
actions_phys = data["actions"].astype(np.float64)
mu = 2.0 / 100 # Re=100 for steady (default Re)
return sensors, forces, actions_phys, mu
def build_scene_data(scene: str, re_codes: List[int] = None):
"""Build feature matrices for a scene.
Returns (Theta_front, Theta_rear_input, Y, feat_names_front, feat_names_rear, scene_info)
where Theta_rear_input is the feature matrix for the rear shared-head model (top channel).
"""
all_feats_front = []
all_feats_rear = []
all_Y = []
all_info = []
if scene == "karman":
if re_codes is None:
re_codes = [50, 100, 200]
for rc in re_codes:
sensors, forces, actions_phys, mu = load_karman_data(rc)
_append_scene_data(sensors, forces, actions_phys, mu, rc,
all_feats_front, all_feats_rear, all_Y, all_info)
elif scene == "steady":
sensors, forces, actions_phys, mu = load_steady_data()
_append_scene_data(sensors, forces, actions_phys, mu, 100,
all_feats_front, all_feats_rear, all_Y, all_info)
else:
raise ValueError(f"Unknown scene: {scene}")
# Stack all data
Theta_front = np.vstack(all_feats_front)
Theta_rear = np.vstack(all_feats_rear)
Y = np.vstack(all_Y)
# Feature names (front has no bias)
feat_names_front = get_feature_names(ALL_FEAT_KEYS, add_bias=False)
feat_names_rear = get_feature_names(ALL_FEAT_KEYS, add_bias=True)
return Theta_front, Theta_rear, Y, feat_names_front, feat_names_rear, all_info
def _append_scene_data(sensors, forces, actions_phys, mu, re_code,
all_feats_front, all_feats_rear, all_Y, all_info):
"""Helper: compute features and append to lists."""
T = sensors.shape[0]
# Memory terms
a_prev = np.zeros((T, 3), dtype=np.float64)
a_prev2 = np.zeros((T, 3), dtype=np.float64)
a_prev[1:] = actions_phys[:-1]
a_prev2[2:] = actions_phys[:-2]
# Dimensionless
dim = compute_dimensionless(sensors, forces, u0=U0, d=20.0)
# Compute features
sym = compute_features(dim, a_prev, a_prev2, mu, alpha_mode=False, include_mu=True)
# Front model: no bias
T_eff = min(sensors.shape[0], actions_phys.shape[0])
Theta_f = build_feature_matrix(sym, ALL_FEAT_KEYS, add_bias=False)
# Rear model (top channel): with bias
Theta_r = build_feature_matrix(sym, ALL_FEAT_KEYS, add_bias=True)
# Remove warmup (2 steps needed for lag/da)
all_feats_front.append(Theta_f[2:])
all_feats_rear.append(Theta_r[2:])
all_Y.append(actions_phys[2:])
all_info.append({"re_code": re_code, "mu": mu, "n_samples": T - 2})
def fit_sindy(Theta, y, thresholds, output_prefix=""):
"""Run SINDy with threshold grid, return results."""
import pysindy as ps
std = np.std(Theta, axis=0)
std = np.where(std < 1e-8, 1.0, std)
Theta_s = Theta / std
results = []
for th in thresholds:
opt = ps.STLSQ(threshold=th, alpha=1e-4, max_iter=25)
opt.fit(Theta_s, y)
coef = np.asarray(opt.coef_, dtype=np.float64).flatten() / std
y_pred = Theta @ coef
ssr = float(np.sum((y - y_pred) ** 2))
sst = float(np.sum((y - np.mean(y)) ** 2) + 1e-12)
r2 = 1.0 - ssr / sst
mae = float(np.mean(np.abs(y - y_pred)))
nz = int(np.sum(np.abs(coef) > 1e-8))
results.append({
"threshold": float(th), "nz": nz, "r2": r2,
"mae": mae, "coef": [float(c) for c in coef],
})
return results
def run(scene: str, out_dir: str, re_codes: List[int] = None):
"""Run SINDy fitting for a scene."""
print(f"\n{'='*60}")
print(f"Scene: {scene}")
print(f"{'='*60}")
Theta_f, Theta_r, Y, fn_f, fn_r, info = build_scene_data(scene, re_codes)
print(f" Front features: {Theta_f.shape}")
print(f" Rear features: {Theta_r.shape}")
print(f" Y shape: {Y.shape}")
# Rear shared-head: fit top channel (ci=2), bottom = -top(Gx)
# We fit both channels independently first for comparison
# Front channel (ci=0, no bias)
print(f"\n --- Front (no bias) ---")
front_results = fit_sindy(Theta_f, Y[:, 0], THRESHOLDS)
best_f = max(front_results, key=lambda r: r["r2"])
print(f" Best: th={best_f['threshold']:.4f} nz={best_f['nz']:2d} R2={best_f['r2']:.6f}")
# Rear shared: fit top (ci=2)
print(f"\n --- Top (rear shared-head) ---")
top_results = fit_sindy(Theta_r, Y[:, 2], THRESHOLDS)
best_t = max(top_results, key=lambda r: r["r2"])
print(f" Best: th={best_t['threshold']:.4f} nz={best_t['nz']:2d} R2={best_t['r2']:.6f}")
# Bottom (for comparison): fit independently
print(f"\n --- Bottom (independent, for comparison) ---")
bot_results = fit_sindy(Theta_r, Y[:, 1], THRESHOLDS)
best_b = max(bot_results, key=lambda r: r["r2"])
print(f" Best: th={best_b['threshold']:.4f} nz={best_b['nz']:2d} R2={best_b['r2']:.6f}")
# Save results
result = {
"scene": scene,
"re_codes": re_codes or "default",
"n_samples_front": Theta_f.shape[0],
"n_features_front": Theta_f.shape[1],
"n_features_rear": Theta_r.shape[1],
"feature_names_front": fn_f,
"feature_names_rear": fn_r,
"front": {
"results": [{k: v for k, v in r.items() if k != "coef"} for r in front_results],
"best": {k: v for k, v in best_f.items() if k != "coef"},
"best_coef": best_f["coef"],
"sparsity_curve": [(r["threshold"], r["nz"], r["r2"]) for r in front_results],
},
"top": {
"results": [{k: v for k, v in r.items() if k != "coef"} for r in top_results],
"best": {k: v for k, v in best_t.items() if k != "coef"},
"best_coef": best_t["coef"],
"sparsity_curve": [(r["threshold"], r["nz"], r["r2"]) for r in top_results],
},
"bottom": {
"results": [{k: v for k, v in r.items() if k != "coef"} for r in bot_results],
"best": {k: v for k, v in best_b.items() if k != "coef"},
"best_coef": best_b["coef"],
"sparsity_curve": [(r["threshold"], r["nz"], r["r2"]) for r in bot_results],
},
"scene_info": info,
}
os.makedirs(out_dir, exist_ok=True)
out_path = os.path.join(out_dir, f"{scene}_sindy.json")
with open(out_path, "w") as f:
json.dump(result, f, indent=2)
print(f"\nSaved: {out_path}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, required=True, choices=["karman", "steady", "all"])
ap.add_argument("--out-dir", type=str,
default=os.path.join(_REPO, "src", "analysis_cloak", "scenes"))
ap.add_argument("--re-codes", type=str, default=None,
help="Comma-separated Re codes for Karman (default: 50,100,200)")
args = ap.parse_args()
re_codes = [int(r) for r in args.re_codes.split(",")] if args.re_codes else None
scenes = ["karman", "steady"] if args.scene == "all" else [args.scene]
for scene in scenes:
sindy_dir = os.path.join(args.out_dir, scene, "sindy")
run(scene, sindy_dir, re_codes)
if __name__ == "__main__":
main()
src/analysis_cloak/common/run_sr_pareto.py
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"""Pareto analysis of SINDy threshold grid.
No external SR library. Runs in any env.
Usage: python run_sr_pareto.py --scene karman
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import List, Tuple
import numpy as np
_THIS = os.path.abspath(os.path.dirname(__file__))
_REPO = os.path.abspath(os.path.join(_THIS, "..", "..", ".."))
def load_sindy(scene: str):
path = os.path.join(_REPO, "src", "analysis_cloak", "scenes", scene, "sindy", f"{scene}_sindy.json")
with open(path) as f:
return json.load(f)
def pareto(points):
sp = sorted(points, key=lambda x: (x[0], x[1]))
front, best = [], float("inf")
for c, e in sp:
if e < best:
front.append((c, e))
best = e
return front
def fmt(fn, coef, th):
ca = np.array(coef, dtype=np.float64)
sc = np.max(np.abs(ca)) if np.max(np.abs(ca)) > 0 else 1.0
mask = np.abs(ca) / sc >= th
terms = [f"{ca[i]:+.4f}*{fn[i]}" for i in range(len(fn)) if mask[i]]
return " ".join(terms) if terms else "0"
def analyze(name, fn, ch):
grid = ch["results"]
pts = [(g["nz"], 1.0 - g["r2"]) for g in grid]
front = pareto(pts)
best = ch["best"]
coef = ch["best_coef"]
print(f"\n {name}:")
for nz, err in front:
r2 = 1.0 - err
for g in grid:
if g["nz"] == nz and abs(1.0 - g["r2"] - err) < 1e-10:
th = g["threshold"]
print(f" nz={nz:2d} R2={r2:.6f} th={th:.4f}")
if nz <= 8:
print(f" {fmt(fn, coef, th)[:120]}")
print(f" Best: R2={best['r2']:.6f}")
return {"channel": name, "best_r2": best["r2"],
"best_nz": sum(1 for c in coef if abs(float(c)) > 1e-8),
"pareto": [{"nz": nz, "r2": 1.0 - e} for nz, e in front]}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", required=True, choices=["karman", "steady"])
args = ap.parse_args()
s = load_sindy(args.scene)
print(f"Pareto SR: {args.scene}")
chs = [("front", s["feature_names_front"], s["front"]),
("top", s["feature_names_rear"], s["top"]),
("bottom", s["feature_names_rear"], s["bottom"])]
results = {"scene": args.scene, "channels": [analyze(*c) for c in chs]}
out = os.path.join(_REPO, "src", "analysis_cloak", "scenes", args.scene, "sr",
f"{args.scene}_sr_pareto.json")
os.makedirs(os.path.dirname(out), exist_ok=True)
with open(out, "w") as f:
json.dump(results, f, indent=2)
print(f"Saved: {out}")
if __name__ == "__main__":
main()
src/analysis_cloak/comparisons/__init__.py
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src/analysis_cloak/comparisons/support_overlap/__init__.py
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src/analysis_cloak/comparisons/support_overlap/run_overlap.py
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"""Support Overlap Analysis: Karman vs Steady.
Compares SINDy support sets at a given sparsity threshold.
"""
from __future__ import annotations
import json
import os
import sys
from typing import Dict, List, Tuple
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
THRESHOLD = 0.002
def load_sindy(scene: str) -> dict:
path = os.path.join(_REPO, "src", "analysis_cloak", "scenes", scene, "sindy", f"{scene}_sindy.json")
with open(path) as f:
return json.load(f)
def get_active(feat_names: List[str], coef: List[float], threshold: float) -> Dict[str, float]:
scale = max(abs(c) for c in coef) if coef and max(abs(c) for c in coef) > 1e-12 else 1.0
active = {}
for name, c in zip(feat_names, coef):
if abs(c) / scale >= threshold:
active[name] = c
return active
def classify(k_active: Dict, s_active: Dict) -> Tuple[List, List, List]:
k_set = set(k_active.keys())
s_set = set(s_active.keys())
return sorted(k_set & s_set), sorted(k_set - s_set), sorted(s_set - k_set)
def feat_group(name: str) -> str:
if name == "bias": return "bias"
if name in ("u_m", "u_a", "u_c", "v_a", "sin_ua", "cos_ua"): return "sensor"
if name in ("Cd_tot", "Cd_rear", "Cl_tot", "Cl_diff"): return "force"
if "lag1" in name: return "memory_lag"
if name.startswith("da"): return "memory_delta"
if name == "mu" or name.startswith("mu_"): return "mu_mod"
return "other"
def main():
print("=" * 60)
print(f"Support Overlap: Karman vs Steady (th={THRESHOLD})")
print("=" * 60)
k = load_sindy("karman")
s = load_sindy("steady")
channels = [
("Front", "feature_names_front", "front"),
("Top (shared)", "feature_names_rear", "top"),
("Bottom", "feature_names_rear", "bottom"),
]
for label, fn_key, ch_key in channels:
print(f"\n--- {label} ---")
fn_k, fn_s = k[fn_key], s[fn_key]
ka = get_active(fn_k, k[ch_key]["best_coef"], THRESHOLD)
sa = get_active(fn_s, s[ch_key]["best_coef"], THRESHOLD)
shared, ko, so = classify(ka, sa)
print(f" Karman nz={len(ka)} Steady nz={len(sa)} Shared={len(shared)}")
for name in shared:
g = feat_group(name)
print(f" {name:20s} K={ka[name]:+9.6f} S={sa[name]:+9.6f} [{g}]")
for name in ko:
print(f" K-ONLY {name:20s} K={ka[name]:+9.6f} [{feat_group(name)}]")
for name in so:
print(f" S-ONLY {name:20s} S={sa[name]:+9.6f} [{feat_group(name)}]")
if __name__ == "__main__":
main()
src/analysis_cloak/scenes/karman/closed_loop/re200_freq_test.json
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{
"si800": 0.7044314206319138,
"si400": 0.7877798695065495,
"si200": 0.6587465172737008
}
src/analysis_cloak/scenes/karman/closed_loop/test_re200_freq.py
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"""Re200 control frequency experiment.
Tests if higher control frequency improves Re200 performance.
Uses v23 predictor (v2 coeffs + front bias=0 + rear shared-head).
Usage:
conda run -n pycuda_3_10 python test_re200_freq.py --device 2
"""
import argparse
import json
import os
import sys
from collections import deque
import numpy as np
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField
from src.analysis_crossre.scripts.utils import (
nu_from_re, action_to_physical, scale_action, build_karman_cloak_env,
add_pinball, compute_physical_symbols, save_vorticity_png,
vorticity_from_ddf, compute_similarity, load_legacy_configs,
)
from src.analysis_crossre.scripts.cfg import (
OUTPUT_DIR, FIFO_LEN, CONV_LEN, S_DIM, ACTION_SCALE, ACTION_BIAS, U0, CONFIG_DIR,
)
DATA_TYPE = np.float32
RE_CODE = 200
V2_RESULTS = os.path.join(OUTPUT_DIR, "sindy", "sindy_results_v2.json")
# Feature keys matching v2 layout
V2_FEAT_KEYS = [
"u_m", "u_a", "u_c", "v_a",
"Fx_tot", "Fx_rear", "Fy_tot", "Fy_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
]
V2_MU_KEYS = ["mu", "mu_u_a", "mu_v_a", "mu_Fx_tot", "mu_Fy_diff", "mu_Fy_tot"]
def apply_G_raw(obs_slice, a_prev, a_prev2):
G_obs = np.zeros(12, dtype=np.float64)
G_obs[0] = obs_slice[4]
G_obs[1] = -obs_slice[5]
G_obs[2] = obs_slice[2]
G_obs[3] = -obs_slice[3]
G_obs[4] = obs_slice[0]
G_obs[5] = -obs_slice[1]
G_obs[6] = obs_slice[6]
G_obs[7] = -obs_slice[7]
G_obs[8] = obs_slice[10]
G_obs[9] = -obs_slice[11]
G_obs[10] = obs_slice[8]
G_obs[11] = -obs_slice[9]
G_a_prev = np.array([-a_prev[0], -a_prev[2], -a_prev[1]], dtype=np.float64)
G_a_prev2 = np.array([-a_prev2[0], -a_prev2[2], -a_prev2[1]], dtype=np.float64)
return G_obs, G_a_prev, G_a_prev2
def build_feat_vec(obs_slice, a_prev, a_prev2, mu, add_bias):
sensors = obs_slice[0:6].astype(np.float64).reshape(1, 6)
forces = obs_slice[6:12].astype(np.float64).reshape(1, 6)
ap = a_prev.astype(np.float64).reshape(1, 3)
ap2 = a_prev2.astype(np.float64).reshape(1, 3)
sym = compute_physical_symbols(sensors, forces, ap, ap2)
sym["mu"] = np.array([mu])
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
sym["mu_Fx_tot"] = sym["Fx_tot"] * mu
sym["mu_Fy_diff"] = sym["Fy_diff"] * mu
sym["mu_Fy_tot"] = sym["Fy_tot"] * mu
vals = []
if add_bias:
vals.append(1.0)
for k in V2_FEAT_KEYS:
vals.append(float(sym[k][0]))
for k in V2_MU_KEYS:
vals.append(float(sym[k][0]))
return np.array(vals, dtype=np.float64)
def load_coefs(path):
with open(path) as f:
data = json.load(f)
coefs_list = data["cross_re"]["channels"]
coefs_list[0]["best_coef"][0] = 0.0 # zero front bias
return {
"front": {"coef": np.array(coefs_list[0]["best_coef"], dtype=np.float64), "has_bias": True},
"top": {"coef": np.array(coefs_list[2]["best_coef"], dtype=np.float64), "has_bias": True},
}
def predict(obs_slice, a_prev, a_prev2, coefs, mu):
feat = build_feat_vec(obs_slice, a_prev, a_prev2, mu, add_bias=True)
front = float(feat @ coefs["front"]["coef"])
top = float(feat @ coefs["top"]["coef"])
G_obs, G_a_prev, G_a_prev2 = apply_G_raw(obs_slice, a_prev, a_prev2)
feat_G = build_feat_vec(G_obs, G_a_prev, G_a_prev2, mu, add_bias=True)
bottom = -float(feat_G @ coefs["top"]["coef"])
return np.array([front, bottom, top], dtype=np.float64)
def run(re_code, sample_interval, device_id, output_dir):
mu = 2.0 / re_code
label = f"Re{re_code}_si{sample_interval}"
os.makedirs(output_dir, exist_ok=True)
coefs = load_coefs(V2_RESULTS)
cuda_cfg, field_cfg = load_legacy_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(nu_from_re(re_code, u0=U0)))
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
target_states, _ = build_karman_cloak_env(ff, u0=U0, l0=20.0,
sample_interval=sample_interval, fifo_len=FIFO_LEN, data_type=DATA_TYPE)
norm = add_pinball(ff, l0=20.0, u0=U0, sample_interval=sample_interval,
fifo_len=FIFO_LEN, data_type=DATA_TYPE, action_bias=ACTION_BIAS)
ff.restore_ddf();
ff.apply_ddf()
fifo = deque(maxlen=FIFO_LEN)
bias_action = scale_action(np.zeros(3, dtype=np.float32), scale=ACTION_SCALE,
bias=ACTION_BIAS, u0=U0, n_total_bodies=7)
for _ in range(FIFO_LEN):
ff.run(sample_interval, bias_action)
fifo.append(ff.obs.copy()[2:14])
sens_sc = []
a_prev = action_to_physical(np.zeros((1,3), dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0).flatten()
a_prev2 = a_prev.copy()
n_steps = 100
for _ in range(n_steps):
obs = fifo[-1] if len(fifo) > 0 else np.zeros(12, dtype=np.float32)
omega = predict(obs, a_prev, a_prev2, coefs, mu)
norm_a = (omega / U0 - np.array(ACTION_BIAS, dtype=np.float64)) / ACTION_SCALE
norm_a = np.clip(norm_a, -1.0, 1.0).astype(np.float32)
action_arr = scale_action(norm_a, scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0, n_total_bodies=7)
ff.run(sample_interval, action_arr)
obs_new = ff.obs.copy()[2:14]
fifo.append(obs_new)
sens_sc.append(obs_new[0:6])
a_prev2 = a_prev.copy()
a_prev = omega.copy()
sens_arr = np.array(sens_sc, dtype=np.float32)
sim = compute_similarity(target_states, sens_arr, CONV_LEN)
print(f" {label}: similarity={sim:.4f}")
del ff
return sim
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=2)
args = ap.parse_args()
out_dir = os.path.join(_PROJ, "src", "analysis_cloak", "scenes", "karman", "closed_loop")
os.makedirs(out_dir, exist_ok=True)
intervals = [800, 400, 200]
results = {}
for si in intervals:
sim = run(RE_CODE, si, args.device, out_dir)
results[f"si{si}"] = sim
print(f"\nRe200 frequency test results:")
for si, sim in results.items():
print(f" SAMPLE_INTERVAL={si}: similarity={sim:.4f}")
with open(os.path.join(out_dir, "re200_freq_test.json"), "w") as f:
json.dump(results, f, indent=2)
if __name__ == "__main__":
main()
src/analysis_cloak/scenes/karman/sindy/karman_sindy.json
+508
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@@ -0,0 +1,508 @@
{
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}
src/analysis_cloak/scenes/karman/sr/karman_sr_pareto.json
+94
View File
@@ -0,0 +1,94 @@
{
"scene": "karman",
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}
src/analysis_cloak/scenes/steady/closed_loop/gen_steady_data.py
+170
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"""Generate steady cloak data.
Steady cloak uses open-loop constant rotation (no PPO model).
The environment has pinball cylinders but no disturbance cylinder.
Target is clean parabolic inflow (constant sensor values).
Usage:
conda run -n pycuda_3_10 python gen_steady_data.py --device 2
"""
import argparse
import os
import sys
from collections import deque
import numpy as np
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField
from LegacyCelerisLab import utils as legacy_utils
# Point to analysis_crossre configs
CONFIG_DIR = os.path.join(_PROJ, "src", "analysis_crossre", "configs")
OUTPUT_DIR = os.path.join(_PROJ, "src", "analysis_cloak", "scenes", "steady", "closed_loop")
U0 = 0.01
L0 = 20
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
DATA_TYPE = np.float32
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=2)
ap.add_argument("--action-top", type=float, default=5.1, help="Top cylinder omega factor")
ap.add_argument("--action-bottom", type=float, default=-5.1, help="Bottom cylinder omega factor")
ap.add_argument("--n-steps", type=int, default=200)
args = ap.parse_args()
os.makedirs(OUTPUT_DIR, exist_ok=True)
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
ff = FlowField(field_cfg, cuda_cfg, device_id=args.device)
NY = ff.FIELD_SHAPE[1]
NX = ff.FIELD_SHAPE[0]
# Step 1: Add only pinball cylinders (NO disturbance cylinder)
# Front cylinder
ff.add_cylinder((30 * L0, (NY - 1) / 2, 0), L0 / 2)
# Bottom cylinder
ff.add_cylinder((31.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0), L0 / 2)
# Top cylinder
ff.add_cylinder((31.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0), L0 / 2)
# 3 sensors at x=40*L0
ff.add_sensor((40 * L0, (NY - 1) / 2 + 2 * L0, 0), L0 / 4)
ff.add_sensor((40 * L0, (NY - 1) / 2, 0), L0 / 4)
ff.add_sensor((40 * L0, (NY - 1) / 2 - 2 * L0, 0), L0 / 4)
n_bodies = ff.obs.size // 2
print(f"Bodies: {n_bodies} (3 pinball + 3 sensors)")
# Step 2: Stabilize with zero action
stabilize_steps = int(4 * NX / U0)
print(f"Stabilising ({stabilize_steps} steps)...")
ff.run(stabilize_steps, np.zeros(n_bodies, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf()
# Step 3: Record target (steady inflow, no pinball) - we already have clean inflow as target
# For steady cloak, target is just the mean of clean parabolic inflow
# But since we built env WITH pinball, we need target separately
# Actually for steady cloak, target is just the inlet profile at sensor positions
# Let's compute it: run with all bodies but minimal disturbance
# The "target" is simply the flow that would exist without pinball
# Since this is a parabolic channel, the sensors would read the undisturbed parabolic profile
# For simplicity, record the initial steady-state sensor values as target
ff.restore_ddf()
ff.apply_ddf()
target_list = []
for i in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, np.zeros(n_bodies, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()
# obs for 6 bodies: [sensor0(2), sensor1(2), sensor2(2), cyl0(2), cyl1(2), cyl2(2)]
# We only want sensor values: indices 0..5
target_list.append(obs_slice[0:6].copy())
target_states = np.array(target_list, dtype=np.float32)
target_mean = np.mean(target_states, axis=0)
print(f"Target mean (steady inflow at sensors): {target_mean}")
# Step 4: Run with constant rotation
ff.restore_ddf()
ff.apply_ddf()
constant_action = np.zeros(n_bodies, dtype=DATA_TYPE)
constant_action[n_bodies - 3] = 0.0 * U0 # front
constant_action[n_bodies - 2] = float(args.action_bottom * U0) # bottom
constant_action[n_bodies - 1] = float(args.action_top * U0) # top
print(f"Constant action: {constant_action}")
# Stabilize to the controlled state
print("Running controlled steady state...")
for i in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, constant_action)
# Record controlled data
sensors_list = []
forces_list = []
actions_list = []
for step in range(args.n_steps):
ff.run(SAMPLE_INTERVAL, constant_action)
obs_slice = ff.obs.copy()
sensors_list.append(obs_slice[0:6].copy())
forces_list.append(obs_slice[6:12].copy()) # pinball forces are at indices 6-11
actions_list.append(constant_action[n_bodies - 3:n_bodies].copy())
sensors_arr = np.array(sensors_list, dtype=np.float32)
forces_arr = np.array(forces_list, dtype=np.float32)
actions_arr = np.array(actions_list, dtype=np.float32)
print(f"Sensors shape: {sensors_arr.shape}")
print(f"Forces shape: {forces_arr.shape}")
print(f"Sensor mean (controlled): {np.mean(sensors_arr, axis=0)}")
print(f"Force mean (controlled): {np.mean(forces_arr, axis=0)}")
# Save
out_path = os.path.join(OUTPUT_DIR, "steady_data.npz")
np.savez(out_path, sensors=sensors_arr, forces=forces_arr,
actions=actions_arr, target_states=target_states,
action_constant=constant_action)
print(f"Saved: {out_path}")
# Also save vorticity of final state
ff.get_ddf()
ddf = ff.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf[:, :, 1] + ddf[:, :, 5] + ddf[:, :, 8] - ddf[:, :, 3] - ddf[:, :, 6] - ddf[:, :, 7]) / U0
uy = (ddf[:, :, 2] + ddf[:, :, 5] + ddf[:, :, 6] - ddf[:, :, 4] - ddf[:, :, 7] - ddf[:, :, 8]) / U0
omega = np.gradient(uy, axis=1) - np.gradient(ux, axis=0)
try:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
abs_o = np.abs(omega[np.isfinite(omega)])
vmax = float(np.percentile(abs_o, 99.5)) if abs_o.size > 0 else 1.0
if vmax <= 0:
vmax = 1.0
fig, ax = plt.subplots(figsize=(12, 5))
im = ax.imshow(omega, origin="lower", aspect="equal", cmap="RdBu_r",
vmin=-vmax, vmax=vmax, extent=(0, NX - 1, 0, NY - 1))
ax.set_title(f"Steady cloak: action=[0, {args.action_bottom}, {args.action_top}]*U0")
fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
fig.savefig(os.path.join(OUTPUT_DIR, "vorticity_steady.png"), dpi=150, bbox_inches="tight")
plt.close(fig)
print(f"Vorticity saved to {OUTPUT_DIR}/vorticity_steady.png")
except ImportError:
print("matplotlib not available, skipping vorticity PNG")
del ff
print("Done")
if __name__ == "__main__":
main()
src/analysis_cloak/scenes/steady/closed_loop/vorticity_steady.png
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src/analysis_cloak/scenes/steady/sindy/steady_sindy.json
+498
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@@ -0,0 +1,498 @@
{
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src/analysis_cloak/scenes/steady/sr/steady_sr_pareto.json
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src/analysis_crossre/analysis_notes.md
+405
View File
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当前这条线的目标，已经不再是“继续把 Kármán cloak 的跨 \(Re_D\) 拟合做得更精致”，而是：**把所有 cloak 任务纳入同一受约束分析框架，用 SINDy 与 SR 并行探索 shared backbone 的几种可能结构。** 这里的关键词不是“立刻统一”，而是“先分开拟合、横向比较、再判断共享到什么程度”。对 coder 来说，接下来最重要的不是继续沿着单一版本号推进，而是切换到**探索型工作流**。
## 一句话总目标
\[
\boxed{
\text{对所有 cloak 场景，在统一变量、统一对称规则、尽量时间尺度显式化的前提下，分别做 SINDy 与 SR，再比较公式、support 与闭环表现，从而识别 shared backbone、scene-specific activation 与 time-scale effects。}
}
\]
## 现在已经比较确定的结论
| 结论 | 当前状态 | 对后续工作的含义 |
|---|---|---|
| Kármán cloak 跨 \(Re_D\) 统一骨架存在 | 已确认 | 可作为第一批强证据，不必再单线深挖很久 |
| 控制律满足镜像等变结构 | 已确认 | 所有 cloak 后续都应沿用这套 \(G\) 规则 |
| front 不需要 bias | 已确认 | front 默认 odd 结构 |
| rear shared-head 有价值 | 已确认 | rear 默认优先尝试共享头，而不是无约束独立 |
| 无量纲化不是问题根源 | 已确认 | 变量统一应继续保留 |
| 所有 cloak 是否共享同一骨架 | 未知 | 当前最核心的新问题 |
| 采样周期是否掩盖了高 Re 或某些 cloak 的潜力 | 未知 | 要纳入时间尺度探索线，而不是忽略 |
## 思路转变
之前的工作方式更像：
1. 选一个场景
2. 试一个版本
3. 比较 one-step 与闭环
4. 再改下一个版本
这个方式在“证明跨 Re 骨架存在”阶段是有效的，但接下来不够用了。现在需要转成：
1. 先统一所有场景共享的变量与约束
2. 每个场景分别做 SINDy 与 SR
3. 输出可横向比较的结果包
4. 再在比较结果上判断哪些是 backbone，哪些是场景特有项
也就是说，**版本号思路要让位给实验矩阵思路**。
## 后续要探索的几种结构可能
后面的代码与结果组织，不应默认只有一种真相，而要明确服务于几种可能。
### 可能性 A
所有 cloak 共享同一个核心骨架，只是系数不同。
### 可能性 B
所有 cloak 共享同一批核心项，但不同场景会激活不同附加项。
### 可能性 C
表面上不同 cloak 的 support 不同，但经过时间尺度显式化后会明显收敛。
### 可能性 D
cloak family 不是单一骨架，而是分成若干子家族，例如 steady/Kármán 一类，单涡/erase 一类。
后续计划必须让每种可能都能被检验，而不是在一开始就把工作锁死在某个预设答案上。
## 新的执行总框架
对 coder 来说，接下来最实用的组织方式是三条并行主线。
| 线 | 目标 | 输出 |
|---|---|---|
| A. 统一表征线 | 统一变量、约束、时间尺度写法 | 所有场景共享的 feature builder |
| B. 分场景拟合线 | 每个 cloak 分别做 SINDy 与 SR | 每个场景的 support、公式、闭环表现 |
| C. 横向比较线 | 比较场景之间的共性与差异 | overlap 表、shared-backbone 假设检验 |
注意：这三条线不是先后串行，而是 A 先到可用、B 尽快开始、C 随第一批结果启动。
## 具体执行路线图
## 阶段 0
## 固定统一接口
这一阶段的目标不是新结果，而是防止后面每个场景又各写一套特征逻辑。
### 0.1 统一 primitive variables
所有 cloak 场景统一输出以下 primitive variables：
- 无量纲 sensor：\(\hat u, \hat v\)
- 力系数：\(C_D, C_L\)
- 无量纲控制：\(\alpha\)
- lagged \(\alpha\)
- action increment 或其时间尺度显式化版本
- \(\mu=1/Re_D\)
- 场景元数据：scene id、Re、control interval、target type
### 0.2 固定正确的 \(G\) 算子
所有场景都用同一套 \(G\) 规则：
\[
(\alpha_F,\alpha_T,\alpha_B) \mapsto (-\alpha_F,-\alpha_B,-\alpha_T)
\]
且其 lag、increment、sensor、force 的符号规则必须与此保持一致。coder 后续不能再为某个单独场景临时改 \(G\)。
### 0.3 时间尺度先显式化，不急于一步到位
当前不要求立刻找到最终完美的时间尺度写法，但要求先把离散 cadence 从“隐变量”变成“显变量”。最低要求：
- 每个场景记录 control interval \(\Delta t_c\)
- lag 与 \(\Delta a\) 的定义显式绑定 \(\Delta t_c\)
- 后续比较不同场景、不同采样率时，不允许再把“1 个采样步”当成无条件可比的量
### 0.4 统一 feature builder
统一生成三类特征：
| 层级 | 内容 | 用途 |
|---|---|---|
| core | 无量纲 sensor、\(C_D,C_L\)、\(\alpha^-\)、\(\Delta\alpha^-\)、\(\mu\) | 所有模型共用 |
| derived | 对称/反对称组合、总量/差量 | SINDy 主库 |
| time-scale | 显式含 \(\Delta t_c\) 的增量或导数近似 | 采样率影响分析 |
这一阶段结束的标志不是拟合结果，而是：**所有 cloak 场景可以通过同一个 builder 产生可比较特征。**
## 阶段 1
## 所有 cloak 分开做第一轮 SINDy
这一步是当前最先应该全面展开的。
### 场景优先级
建议按两层推进，不要求一次所有场景做到同等深度。
#### 第一层重点场景
- Kármán cloak
- steady cloak
这两个场景要做完整输出：
- front odd + rear shared-head 约束 SINDy
- one-step
- 关键闭环
- support 稳定性
#### 第二层扩展场景
- 单涡 cloak
- erase
- 其他已有 cloak
这批先做轻量版：
- 同一变量空间下的 separate fit
- one-step
- support 与公式形态
- 必要时再补闭环
### 每个场景必须输出什么
每个场景第一轮 SINDy 结果，必须统一输出下面这些文件或表：
| 输出 | 说明 |
|---|---|
| best support | 最优 support 列表 |
| sparsity curve | 稀疏度-误差曲线 |
| front / rear 主项表 | 各通道的主导项与系数 |
| one-step metrics | R²、RMSE |
| selected closed-loop metric | similarity 或等价指标 |
| support stability | 对 threshold / window / bootstrap 的稳定性 |
### 约束默认值
除非某个场景明确被数据否定，否则第一轮都默认：
- front no-bias
- front odd structure
- rear shared-head
- correct \(G\) consistency
也就是说，现在不再把“独立三通道”当默认，而是把“有结构约束”当默认。
## 阶段 2
## 所有 cloak 分开做第一轮 SR
SR 现在与 SINDy 同步启动，但它的任务是“受限压缩”，不是自由乱搜。
### SR 输入规则
SR 只能使用：
- 阶段 0 的统一变量
- 阶段 1 的 SINDy 已筛出主项及其邻近项
- 受限运算集合
建议 SR 运算集合仍限制为：
- 加减乘
- protected divide
- 少量 square
- 如确有必要，再有限放开 tanh
暂不允许：
- raw trig 乱搜
- 高次幂
- 指数
- 深层嵌套
### 每个场景 SR 必须输出什么
| 输出 | 说明 |
|---|---|
| shortest acceptable formula | 最短可接受公式 |
| pareto front | 复杂度 vs 误差 |
| 与 SINDy 支持集的关系 | 是否压缩、是否改写、是否合并了主项 |
| 闭环表现 | 至少对最佳 SR 公式做一版关键闭环 |
SR 的第一轮目标不是直接找到最终公式，而是回答：
- 某些场景是否能被更短闭式描述
- 某些场景之间是否出现同形公式
- 某些 SINDy 重要项是否在 SR 中被统一吸收
## 阶段 3
## 横向比较与 shared-backbone 检验
当第一批场景的 SINDy 和 SR 都有结果后，马上进入横向比较，不要等所有场景都做完才比较。
### 3.1 support overlap 分析
至少做下面三种 overlap：
- Kármán vs steady
- Kármán vs 单涡
- steady vs 单涡
比较结果建议分成三类：
| 类别 | 含义 |
|---|---|
| shared core | 多数场景共同出现 |
| scene-enhanced | 多场景可见，但某场景更强 |
| scene-specific | 只在个别场景出现 |
### 3.2 SR 公式形态比较
不能只比 support，还要比公式结构。例如检查：
- 是否都包含同类 force feedback 核心
- 是否都包含同类 memory 核心
- 是否只是在某些场景多出周期项或瞬态项
### 3.3 shared-backbone 假设检验
当横向比较有了第一批证据后，再分别检验：
- steady 是否是 Kármán 的子模型
- 单涡是否是 core + history augmentation
- 是否存在所有 cloak 共用的最小 core
- 是否更适合分成 2~3 个子家族
注意：这一步才开始认真讨论“共享到什么程度”，不是一开始就联合拟合总公式。
## 阶段 4
## 时间尺度探索线
这条线现在应并行启动，但不必抢在全部场景前面。
### 当前目标
不是立即得到采样率最终结论，而是先做两件事：
1. 让现有特征显式包含 \(\Delta t_c\)
2. 看显式化后，不同场景的 support 是否更收敛
### 第一批测试建议
先在最关键两个场景上做：
- Kármán cloak
- steady cloak
对比：
- 旧的 discrete lag / \(\Delta a\)
- 显式带 \(\Delta t_c\) 的版本
看：
- support 是否变化
- SR 公式是否更收敛
- 不同采样率下是否更容易迁移
这条线的定位是：**逐步把采样率实现方式从物理骨架里剥离出来。**
## coder 现在最应该换掉的习惯
下面几条是执行层面的明确要求。
### 不再做的事
- 不再只围绕 Kármán across Re 单线深挖
- 不再把版本号升级当成主要组织方式
- 不再只看 one-step 就判断某条线值不值得做
- 不再在 raw feature 上做自由 SR
### 接下来默认要做的事
- 所有 cloak 进入同一 builder
- 每个场景都做 separate SINDy + separate SR
- 每个结果都输出 support、公式、闭环三类信息
- 结果出来后立即做横向比较
## 建议的文件与结果组织方式
建议从“按版本存结果”改成“按场景 × 方法存结果”。
### 建议目录
```text
analysis_cloak/
common/
feature_builder.py
symmetry.py
time_scale.py
scenes/
karman/
sindy/
sr/
closed_loop/
steady/
sindy/
sr/
closed_loop/
monopole/
sindy/
sr/
closed_loop/
erase/
sindy/
sr/
closed_loop/
comparisons/
support_overlap/
formula_shape/
shared_backbone_tests/
```
### 每个场景的统一结果表
每个场景至少生成一张 summary 表，包含：
| method | sparsity | one-step | closed-loop | key terms | notes |
|---|---:|---:|---:|---|---|
| SINDy | | | | | |
| SR | | | | | |
这样后面比较时不会再陷入“某个版本某次试验表现不错，但很难横向放一起看”的状态。
## 最小可执行计划
如果 coder 需要一个最小可执行版本，按下面顺序做即可。
### 本轮必须完成
1. 统一所有 cloak 的 feature builder
2. 跑 Kármán 与 steady 的第一轮 separate SINDy
3. 跑 Kármán 与 steady 的第一轮受限 SR
4. 输出 Kármán vs steady 的 support overlap 与公式形态比较
### 下一轮扩展
5. 把单涡 cloak 纳入同样流程
6. 对 lag / \(\Delta a\) 做 \(\Delta t_c\) 显式化版本
7. 检查显式化前后 support 是否更收敛
### 再下一轮
8. 开始 shared-backbone hypothesis tests
9. 决定是“统一骨架 + 场景调制”，还是“若干子家族骨架”
10. 再决定是否需要拟联合总公式
## 当前工作的直接收束
因此，接下来给 coder 的总原则应明确改写为：
\[
\boxed{
\text{当前阶段的重点不是继续优化某一个跨 Re 版本，而是切换到场景化探索：所有 cloak 先分开做 SINDy 与 SR，再横向比较 support、公式与闭环，从而判断 shared backbone 到底成立到什么程度。}
}
\]
这意味着后续真正要回答的问题已经变成：
- 哪些项是所有 cloak 的 shared core
- 哪些项是 Kármán、steady、单涡等场景的激活差异
- 时间尺度写法会不会改变这种比较结果
- SR 能否把若干场景的公式压缩成更统一的形态
只有这样，后面的统一控制律才不会停留在“跨 Re 的局部现象”，而会真正推进到你现在更关心的问题：**cloak family 的物理骨架到底是什么。**
src/analysis_crossre/configs/config_cuda.json
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{
"multi_gpu": false,
"gpu_connection": "NVLink",
"required_cuda_capability": "7.0",
"threads_per_block": 128,
"X_1U": 128,
"Y_1U": 32,
"Z_1U": 1
}
src/analysis_crossre/configs/config_flowfield.json
+13
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@@ -0,0 +1,13 @@
{
"data_type": "FP32",
"dimensionality": 2,
"lattice": 9,
"field_dim_in_U": [10, 16, 1],
"viscosity": 0.004,
"velocity": 0.01,
"boundary_conditions": {
"x": ["parabolic", "outflow"],
"y": ["noslip", "noslip"],
"z": ["none", "none"]
}
}
src/analysis_crossre/scripts/cfg.py
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# analysis_crossre/scripts/cfg.py
"""Configuration constants for the cross-Re Karman cloak analysis."""
import os
from typing import List, Dict, Tuple
# -- Paths -------------------------------------------------------------------
_PROJ_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
ANALYSIS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
CONFIG_DIR = os.path.join(ANALYSIS_DIR, "configs")
MODEL_DIR = os.path.join(_PROJ_ROOT, "models")
OUTPUT_DIR = os.path.join(_PROJ_ROOT, "output", "analysis_crossre")
LEGACY_CFD_DIR = os.path.join(_PROJ_ROOT, "LegacyCelerisLab")
# -- GPU config --------------------------------------------------------------
DEVICE_ID = 0 # default, override via --device flag
# -- Legacy CFD config paths -------------------------------------------------
CONFIG_CUDA = os.path.join(CONFIG_DIR, "config_cuda.json")
CONFIG_FLOWFIELD_BASE = os.path.join(CONFIG_DIR, "config_flowfield.json")
# -- Physics constants -------------------------------------------------------
U0 = 0.01 # inlet velocity (lattice units)
D_CYL = 20.0 # single cylinder diameter (lattice units)
D_REF = 40.0 # reference length = 2 * D (used for code "Re")
L0 = 20.0 # base length unit (lattice)
# -- Geometry (Karman cloak standard, all in lattice units) ------------------
# Legacy grid: 1280 x 512
NX = 1280
NY = 512
CENTER_Y = (NY - 1) / 2.0
# Disturbance cylinder
DIST_CENTER = (10.0 * L0, CENTER_Y) # (200, 255.5)
DIST_RADIUS = L0 # 20
# Downstream sensors (at x = 40*L0)
SENSOR_RADIUS = L0 / 4 # 5
SENSOR_CENTERS = [
(40.0 * L0, CENTER_Y + 2.0 * L0), # sensor0 (top)
(40.0 * L0, CENTER_Y), # sensor1 (mid)
(40.0 * L0, CENTER_Y - 2.0 * L0), # sensor2 (bottom)
]
# Pinball cylinders
PINBALL_RADIUS = L0 / 2 # 10
FRONT_CENTER = (30.0 * L0, CENTER_Y) # (600, 255.5)
BOTTOM_CENTER = (31.3 * L0, CENTER_Y - 0.75 * L0) # (626, 240.5)
TOP_CENTER = (31.3 * L0, CENTER_Y + 0.75 * L0) # (626, 270.5)
# -- Sampling parameters ----------------------------------------------------
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
CONV_LEN = 30
STABILIZE_STEPS = int(4 * NX / U0)
# -- DRL parameters ----------------------------------------------------------
S_DIM = 12 # sensor[6] + force[6]
A_DIM = 3
ACTION_SCALE = 8.0
ACTION_BIAS = [0.0, -4.0, 4.0] # front, bottom, top (multiply by U0 later)
# -- Re definition ----------------------------------------------------------
# "Re_code" uses reference length 2*D (D_REF = 40), matching model file naming.
# The true physical Reynolds number is Re_D = Re_code / 2.
# Re_code=100 -> Re_D=50 (default case)
def nu_from_re(re_code: float) -> float:
"""Calculate kinematic viscosity from code Reynolds number."""
return U0 * D_REF / re_code
# Re cases: (re_code, model_name) -- None for validation cases with no model
RE_CASES_TRAIN: List[Tuple[int, str]] = [
(50, "d1a3o12_re50"),
(100, "d1a3o12_re100"),
(200, "d1a3o12_re200"),
(400, "d1a3o12_re400"),
]
RE_CASES_VALIDATION: List[int] = [35, 70, 150]
ALL_RE_CODES: List[int] = [c[0] for c in RE_CASES_TRAIN] + RE_CASES_VALIDATION
RE_LABEL_MAP: Dict[int, str] = {
50: "Re50 (Re_D=25)",
100: "Re100 (Re_D=50)",
200: "Re200 (Re_D=100)",
400: "Re400 (Re_D=200)",
35: "Re35 (Re_D=17.5)",
70: "Re70 (Re_D=35)",
150: "Re150 (Re_D=75)",
}
src/analysis_crossre/scripts/phase1_infer.py
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# analysis_crossre/scripts/phase1_infer.py
"""Phase 1: verify legacy-CFD + PPO inference across Karman cloak Re cases.
Usage::
conda run -n pycuda_3_10 python phase1_infer.py \\
--re 100 --device 0
conda run -n pycuda_3_10 python phase1_infer.py \\
--re all --device 2
Output for each Re case under ``output/analysis_crossre/re{re_code}/``:
- ``config.json`` — runtime parameters
- ``norm.json`` — normalisation factors
- ``target.npz`` — target sensor signal (disturbance only, no pinball)
- ``uncontrolled.npz`` — sensor / force / obs time series (zero-action)
- ``controlled.npz`` — sensor / force / obs / action time series (PPO)
- ``vorticity_uncontrolled.png`` — final-step vorticity, uncontrolled
- ``vorticity_controlled.png`` — final-step vorticity, controlled
- ``rewards.npz`` — step-by-step reward for controlled rollout
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
# Add workspace root so LegacyCelerisLab is importable
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField # noqa: E402
from utils import ( # noqa: E402
nu_from_re,
load_legacy_configs,
build_karman_cloak_env,
add_pinball,
build_observation,
scale_action,
load_ppo_model,
save_vorticity_png,
vorticity_from_ddf,
compute_similarity,
ACTION_SMOOTH_WEIGHT,
)
from cfg import ( # noqa: E402
CONFIG_DIR,
CONFIG_CUDA,
CONFIG_FLOWFIELD_BASE,
OUTPUT_DIR,
MODEL_DIR,
SAMPLE_INTERVAL,
FIFO_LEN,
CONV_LEN,
S_DIM,
A_DIM,
ACTION_SCALE,
ACTION_BIAS,
U0,
STABILIZE_STEPS,
RE_CASES_TRAIN,
RE_CASES_VALIDATION,
RE_LABEL_MAP,
)
DATA_TYPE = np.float32
def run_single_re(
re_code: int,
model_path: Optional[str],
device_id: int,
output_root: str,
*,
n_infer_steps: int = 200,
) -> dict:
"""Run full inference pipeline for one Re case.
Parameters
----------
re_code : int
Code Reynolds number (reference length = 2D).
model_path : str or None
Path to PPO .zip file. None = only uncontrolled runs.
device_id : int
GPU device ID.
output_root : str
Root output directory for this case.
n_infer_steps : int
Number of inference steps (each = SAMPLE_INTERVAL LBM steps).
Returns summary dict with similarity scores etc.
"""
os.makedirs(output_root, exist_ok=True)
# -- 0. Prepare config ---------------------------------------------------
nu = nu_from_re(re_code, u0=U0)
label = RE_LABEL_MAP.get(re_code, f"Re{re_code}")
print(f"\n{'='*60}")
print(f"Case: {label} viscosity={nu:.6f} device={device_id}")
print(f"{'='*60}")
# Save run config
with open(os.path.join(output_root, "config.json"), "w") as f:
json.dump({
"re_code": re_code,
"nu": nu,
"u0": U0,
"sample_interval": SAMPLE_INTERVAL,
"fifo_len": FIFO_LEN,
"conv_len": CONV_LEN,
"device_id": device_id,
"model_path": model_path,
}, f, indent=2)
# -- 1. Load legacy CFD configs, adjust viscosity ------------------------
cuda_cfg, field_cfg = load_legacy_configs(CONFIG_DIR)
# Override viscosity for this Re
field_cfg = field_cfg._replace(viscosity=float(nu))
# -- 2. Build flow field + dist + sensors, record target -----------------
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
target_states, env_info = build_karman_cloak_env(
ff, u0=U0, l0=20.0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE,
)
np.savez(os.path.join(output_root, "target.npz"),
target_states=target_states)
# -- 3. Add pinball, compute norm, bias rollout --------------------------
norm = add_pinball(
ff, l0=20.0, u0=U0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE,
action_bias=ACTION_BIAS,
)
save_states = norm.pop("save_states", None)
# Save norm (without save_states which is a big array)
norm_for_json = {k: v for k, v in norm.items() if not isinstance(v, np.ndarray)}
with open(os.path.join(output_root, "norm.json"), "w") as f:
json.dump(norm_for_json, f, indent=2)
print(f" norm saved to {output_root}/norm.json")
# -- 4. Uncontrolled rollout ---------------------------------------------
print(" uncontrolled rollout ...")
ff.restore_ddf()
ff.apply_ddf()
fifo = deque(maxlen=FIFO_LEN)
sens_list, forc_list, obs_list = [], [], []
for step in range(n_infer_steps):
ff.run(SAMPLE_INTERVAL, np.zeros(7, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[2:14]
fifo.append(obs_slice)
sens_list.append(obs_slice[0:6])
forc_list.append(obs_slice[6:12])
obs = build_observation(obs_slice, norm)
obs_list.append(obs)
np.savez(os.path.join(output_root, "uncontrolled.npz"),
sensors=np.array(sens_list, dtype=np.float32),
forces=np.array(forc_list, dtype=np.float32),
obs=np.array(obs_list, dtype=np.float32))
# Vorticity at final step
omega_unc = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(
os.path.join(output_root, "vorticity_uncontrolled.png"),
omega_unc,
title=f"{label} uncontrolled",
)
# Also save macro field snapshot
macro_unc = {"ux": [], "uy": [], "rho": []} # placeholder; vorticity PNG is enough for Phase 1
# -- 5. Controlled rollout (if model available) --------------------------
result = {"re_code": re_code, "uncontrolled": True, "controlled": False}
if model_path is not None and os.path.isfile(model_path):
print(f" loading model: {model_path}")
model = load_ppo_model(model_path, device=f"cuda:{device_id}")
model.set_random_seed(0)
print(f" controlled rollout ({n_infer_steps} steps) ...")
ff.restore_ddf()
ff.apply_ddf()
# Re-bias the FIFO (as in env.__init__)
bias_action = scale_action(
np.array([0.0, 0.0, 0.0], dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0, n_total_bodies=7,
)
for i in range(FIFO_LEN):
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, bias_action)
finally:
ff.context.pop()
fifo.append(ff.obs.copy()[2:14])
sens_list_c, forc_list_c, obs_list_c = [], [], []
action_list_c, reward_list_c = [], []
obs = np.zeros(S_DIM, dtype=np.float32)
for step in range(n_infer_steps):
action, _states = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
action_list_c.append(action.copy())
# Convert to legacy action array
action_arr = scale_action(
action, scale=ACTION_SCALE, bias=ACTION_BIAS,
u0=U0, n_total_bodies=7,
)
# Run CFD with proper context management (PyTorch shadows legacy ctx)
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
obs_slice = ff.obs.copy()[2:14]
fifo.append(obs_slice)
sens_list_c.append(obs_slice[0:6])
forc_list_c.append(obs_slice[6:12])
obs = build_observation(obs_slice, norm)
obs_list_c.append(obs)
# Compute reward (matching env logic)
states_arr = np.array(list(fifo), dtype=np.float32)
if len(states_arr) >= CONV_LEN:
forces = states_arr[-1, 6:12] / norm["force_norm_fact"]
cd = float((forces[0] + forces[2] + forces[4]) / 3.0)
cl = float((forces[1] + forces[3] + forces[5]) / 3.0)
sim = compute_similarity(target_states, states_arr[:, 0:6], CONV_LEN)
r_cd = np.exp(-abs(cd * 20.0))
r_cl = np.exp(-abs(cl * 80.0))
r_sim = np.exp(-10.0 * abs(sim - 1.0))
reward = min(0.3 * r_cd + 0.4 * r_cl + 0.3 * r_sim, 1.0)
reward_list_c.append(float(reward))
else:
reward_list_c.append(0.0)
np.savez(os.path.join(output_root, "controlled.npz"),
sensors=np.array(sens_list_c, dtype=np.float32),
forces=np.array(forc_list_c, dtype=np.float32),
obs=np.array(obs_list_c, dtype=np.float32),
actions=np.array(action_list_c, dtype=np.float32),
rewards=np.array(reward_list_c, dtype=np.float32))
# Vorticity at final controlled step
omega_con = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(
os.path.join(output_root, "vorticity_controlled.png"),
omega_con,
title=f"{label} controlled (PPO)",
)
avg_reward = float(np.mean(reward_list_c[-100:])) if len(reward_list_c) >= 100 else float(np.mean(reward_list_c))
sim_score = compute_similarity(
target_states, np.array(sens_list_c, dtype=np.float32), CONV_LEN
)
result["controlled"] = True
result["avg_reward_last100"] = avg_reward
result["similarity"] = sim_score
print(f" avg_reward(last100)={avg_reward:.4f} similarity={sim_score:.4f}")
else:
print(f" no model for Re{re_code}, skipping controlled rollout")
# -- 6. Cleanup ----------------------------------------------------------
del ff
# Save result summary
with open(os.path.join(output_root, "result.json"), "w") as f:
json.dump(result, f, indent=2)
return result
def main():
ap = argparse.ArgumentParser(description="Phase 1: cross-Re Karman cloak inference")
ap.add_argument("--re", type=str, default="100",
help='Re case: 50,100,200,400, or "all", or "validation"')
ap.add_argument("--device", type=int, default=0, help="GPU device ID")
ap.add_argument("--steps", type=int, default=200,
help="Number of inference steps per rollout")
args = ap.parse_args()
# Select Re list
selection = args.re.lower()
if selection == "all":
re_list = [c[0] for c in RE_CASES_TRAIN]
elif selection == "validation":
re_list = RE_CASES_VALIDATION
else:
re_list = [int(selection)]
t_start = time.time()
for re_code in re_list:
# Find model path
model_path = None
for rc, mn in RE_CASES_TRAIN:
if rc == re_code:
model_path = os.path.join(MODEL_DIR, "old", f"{mn}.zip")
break
out_dir = os.path.join(OUTPUT_DIR, f"re{re_code}")
result = run_single_re(
re_code, model_path, args.device, out_dir,
n_infer_steps=args.steps,
)
print(f" Done: re{re_code} -> {out_dir}")
elapsed = time.time() - t_start
print(f"\nTotal time: {elapsed:.1f}s")
return 0
if __name__ == "__main__":
sys.exit(main())
src/analysis_crossre/scripts/utils.py
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# analysis_crossre/scripts/utils.py
"""Shared utilities for the cross-Re Karman cloak analysis.
All functions use the LegacyCelerisLab (old) CFD API via ``from LegacyCelerisLab import FlowField``.
Must be run inside ``conda run -n pycuda_3_10``.
"""
from __future__ import annotations
import json
import os
import sys
from collections import deque
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
# -- Import legacy CFD -------------------------------------------------------
# LegacyCelerisLab lives at the repo root; analysis scripts are at
# src/analysis_crossre/scripts/. We need repo root on sys.path.
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from LegacyCelerisLab import FlowField # noqa: E402
from LegacyCelerisLab import utils as legacy_utils # noqa: E402
# ---------------------------------------------------------------------------
# Action-smoothing constant (legacy run() internal)
# ---------------------------------------------------------------------------
ACTION_SMOOTH_WEIGHT = 0.1 # used by FlowField.run() internally
def nu_from_re(re_code: float, u0: float = 0.01, d_ref: float = 40.0) -> float:
"""Return kinematic viscosity for a given code Reynolds number.
``re_code`` uses reference length *2*D* = 40.0 (matching model file naming).
"""
return u0 * d_ref / re_code
def load_legacy_configs(config_dir: str) -> Tuple[Any, Any]:
"""Load and return legacy (cuda_config, field_config) from *config_dir*."""
cuda_cfg = legacy_utils.load_cuda_config(
os.path.join(config_dir, "config_cuda.json")
)
field_cfg = legacy_utils.load_flow_field_config(
os.path.join(config_dir, "config_flowfield.json")
)
return cuda_cfg, field_cfg
# ---------------------------------------------------------------------------
# Environment helpers – follow env_karman_cloak_standard.py exactly
# ---------------------------------------------------------------------------
def build_karman_cloak_env(
flow_field: FlowField,
*,
u0: float,
l0: float,
sample_interval: int,
fifo_len: int,
data_type: type,
) -> Tuple[np.ndarray, dict]:
"""Phase 0-1: add dist-cylinder & 3 sensors, stabilize, record target.
Steps (mirrors env.__init__ lines 64-86):
1. add dist_cylinder (id=0)
2. add 3 sensors (id=1,2,3)
3. stabilize run(4*NX/U0, zero-action[4])
4. record FIFO_LEN × run(SAMPLE_INTERVAL, zero[4]), collect obs[2:8]
Returns
-------
target_states : ndarray (FIFO_LEN, 6) — sensor0/1/2 ux,uy
info : dict with n_objects, NX, NY
"""
n_objects_before = flow_field.flag.size # not used, just a marker
# dist cylinder
center = (10.0 * l0, (flow_field.FIELD_SHAPE[1] - 1) / 2, 0.0)
flow_field.add_cylinder(center, l0)
# sensors
for y_off in [2.0, 0.0, -2.0]:
sc = (40.0 * l0, (flow_field.FIELD_SHAPE[1] - 1) / 2 + y_off * l0, 0.0)
flow_field.add_sensor(sc, l0 / 4.0)
n_obj = flow_field.obs.size // 2 # obs is (n_obj, DIM) flat
print(f" bodies before pinball: {n_obj}")
# stabilize
stabilize_steps = int(4 * flow_field.FIELD_SHAPE[0] / u0)
print(f" stabilising ({stabilize_steps} steps)...")
flow_field.run(stabilize_steps, np.zeros(n_obj, dtype=data_type))
# record target (only sensor signals = obs[2:8])
target_states = np.empty((0, 6), dtype=data_type)
for i in range(fifo_len):
flow_field.run(sample_interval, np.zeros(n_obj, dtype=data_type))
new_state = flow_field.obs.copy()[2:8] # sensor0+1+2 velocities
target_states = np.vstack((target_states, new_state))
print(f" target recorded: {target_states.shape}")
return target_states, {"n_objects": n_obj, "NX": flow_field.FIELD_SHAPE[0],
"NY": flow_field.FIELD_SHAPE[1]}
def add_pinball(
flow_field: FlowField,
*,
l0: float,
u0: float,
sample_interval: int,
fifo_len: int,
data_type: type,
action_bias: Optional[Tuple[float, float, float]] = None,
) -> dict:
"""Phase 2-3: add pinball cylinders, stabilize, compute norm, bias rollout.
Steps (mirrors env.__init__ lines 88-117):
1. add front, bottom, top cylinders (id=4,5,6)
2. stabilize run(4*NX/U0, zero-action[7])
3. get_ddf() + save_ddf() (checkpoint of stabilised state)
4. FIFO_LEN × run(SAMPLE_INTERVAL, zero[7]) → compute norm
5. apply_ddf() (restore pre-bias state)
6. FIFO_LEN × run(SAMPLE_INTERVAL, bias-action[7]) → save_states
7. apply_ddf()
Returns dict with norm values.
"""
if action_bias is None:
action_bias = (0.0, -4.0, 4.0) # default cloak bias: front=0, bottom=-4U0, top=4U0
u0_float = float(u0)
n_obj_before = flow_field.obs.size // 2
# add 3 pinball cylinders
ny = flow_field.FIELD_SHAPE[1]
centers = [
(30.0 * l0, (ny - 1) / 2, 0.0),
(31.3 * l0, (ny - 1) / 2 + 0.75 * l0, 0.0),
(31.3 * l0, (ny - 1) / 2 - 0.75 * l0, 0.0),
]
for c in centers:
flow_field.add_cylinder(c, l0 / 2.0)
n_obj = flow_field.obs.size // 2
print(f" bodies after pinball: {n_obj}")
# stabilize
stabilize_steps = int(4 * flow_field.FIELD_SHAPE[0] / u0_float)
print(f" stabilising pinball ({stabilize_steps} steps)...")
flow_field.run(stabilize_steps, np.zeros(n_obj, dtype=data_type))
# checkpoint DDF
flow_field.get_ddf()
flow_field.save_ddf()
# --- norm phase (zero-action) ---
fifo = deque(maxlen=fifo_len)
for i in range(fifo_len):
flow_field.run(sample_interval, np.zeros(n_obj, dtype=data_type))
fifo.append(flow_field.obs.copy()[2:14]) # sensor[6]+force[6]
temp_states = np.array(fifo, dtype=data_type)
force_norm_fact = 6.0 * float(np.max(np.abs(temp_states[:, 6:12])))
sens_deviation = np.mean(temp_states[:, 0:6], axis=0).astype(data_type)
sens_norm_fact = np.zeros(6, dtype=data_type)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp_states[:, i] - sens_deviation[i])))
print(f" norm: force_norm_fact={force_norm_fact:.6f}")
print(f" norm: sens_deviation={sens_deviation}")
print(f" norm: sens_norm_fact={sens_norm_fact}")
# --- bias-action rollout ---
flow_field.apply_ddf()
bias = np.zeros(n_obj, dtype=data_type)
bias[n_obj - 3] = float(action_bias[0] * u0_float) # front
bias[n_obj - 2] = float(action_bias[1] * u0_float) # bottom
bias[n_obj - 1] = float(action_bias[2] * u0_float) # top
print(f" bias action: {bias}")
fifo.clear()
for i in range(fifo_len):
flow_field.run(sample_interval, bias)
fifo.append(flow_field.obs.copy()[2:14])
save_states = np.array(list(fifo), dtype=data_type)
flow_field.apply_ddf()
return {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
"action_bias": list(action_bias),
"save_states": save_states,
}
def build_observation(
obs_slice: np.ndarray,
norm: dict,
) -> np.ndarray:
"""Assemble normalised DRL observation (12-dim) from a single obs[2:14] slice.
``obs_slice`` is 12-element: sensor[0:6] + force[6:12].
Returns clipped 12-dim array in [-1, 1].
"""
forces = obs_slice[6:12] / norm["force_norm_fact"]
sens = (obs_slice[0:6] - norm["sens_deviation"]) / norm["sens_norm_fact"]
obs = np.clip(np.hstack([forces, sens]), -1.0, 1.0).astype(np.float32)
return obs
def action_to_physical(
action_norm: np.ndarray,
*,
scale: float = 8.0,
bias: Tuple[float, float, float] = (0.0, -4.0, 4.0),
u0: float = 0.01,
) -> np.ndarray:
"""Convert normalized action [-1,1] to physical omega (lattice units).
physical_omega[i] = (action_norm[i] * scale + bias[i]) * u0
"""
action_norm = np.asarray(action_norm, dtype=np.float64).reshape(-1, 3)
bias_arr = np.array(bias, dtype=np.float64)
return (action_norm * scale + bias_arr) * u0
def scale_action(
action_norm: np.ndarray,
*,
scale: float = 8.0,
bias: Tuple[float, float, float] = (0.0, -4.0, 4.0),
u0: float = 0.01,
n_total_bodies: int = 7,
) -> np.ndarray:
"""Convert normalised action ([-1,1]^3) to legacy CFD action array.
Returns array of length *n_total_bodies* with cylinders' omegas at the
last 3 slots.
"""
a = np.zeros(n_total_bodies, dtype=np.float32)
omega = (np.array(action_norm, dtype=np.float32) * scale + np.array(bias, dtype=np.float32)) * u0
a[n_total_bodies - 3:] = omega
return a
# ---------------------------------------------------------------------------
# Vorticity & field export
# ---------------------------------------------------------------------------
def vorticity_from_ddf(flow_field: FlowField, u0: float) -> np.ndarray:
"""Compute z-vorticity from current DDF on host."""
flow_field.get_ddf()
ddf = flow_field.ddf.copy().reshape((9, flow_field.FIELD_SHAPE[1], flow_field.FIELD_SHAPE[0])).transpose(2, 1, 0)
ux = (ddf[:, :, 1] + ddf[:, :, 5] + ddf[:, :, 8] - ddf[:, :, 3] - ddf[:, :, 6] - ddf[:, :, 7]) / u0
uy = (ddf[:, :, 2] + ddf[:, :, 5] + ddf[:, :, 6] - ddf[:, :, 4] - ddf[:, :, 7] - ddf[:, :, 8]) / u0
# vorticity = dv/dx - du/dy
omega = np.gradient(uy, axis=1) - np.gradient(ux, axis=0)
return omega.astype(np.float64)
def save_vorticity_png(path: str, omega: np.ndarray, title: str = ""):
"""Save vorticity field as a PNG with symmetric colour bar."""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
abs_o = np.abs(omega[np.isfinite(omega)])
vmax = float(np.percentile(abs_o, 99.5)) if abs_o.size > 0 else 1.0
if vmax <= 0:
vmax = 1.0
ny, nx = omega.shape
fig, ax = plt.subplots(figsize=(min(18, max(8, nx / 60)), min(10, max(3, ny / 40))))
im = ax.imshow(omega, origin="lower", aspect="equal", cmap="RdBu_r",
vmin=-vmax, vmax=vmax, extent=(0, nx - 1, 0, ny - 1))
ax.set_xlabel("x (lattice)")
ax.set_ylabel("y (lattice)")
if title:
ax.set_title(title)
fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04, label=r"$\omega_z$")
fig.tight_layout()
fig.savefig(path, dpi=150, bbox_inches="tight")
plt.close(fig)
# ---------------------------------------------------------------------------
# DTW similarity
# ---------------------------------------------------------------------------
def calc_lag(target: np.ndarray, state: np.ndarray) -> int:
"""Find lag that maximises cross-correlation between two 1-D signals."""
t = target - np.mean(target)
s = state - np.mean(state)
corr = np.correlate(t, s, mode="full")
lags = np.arange(-len(target) + 1, len(target))
return int(lags[np.argmax(corr)])
def calc_dtw_sim(target: np.ndarray, state: np.ndarray) -> float:
"""DTW-based similarity: 1 - (DTW distance / len(target)).
Both are 1-D arrays of possibly different lengths.
"""
n, m = len(target), len(state)
dtw = np.full((n + 1, m + 1), np.inf)
dtw[0, 0] = 0.0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(float(target[i - 1]) - float(state[j - 1]))
dtw[i, j] = cost + min(dtw[i - 1, j], dtw[i, j - 1], dtw[i - 1, j - 1])
return float(1.0 - dtw[n, m] / n)
def compute_similarity(
target_states: np.ndarray,
state_series: np.ndarray,
conv_len: int,
) -> float:
"""Compute lag-compensated DTW similarity over *conv_len* window.
Matches the reward logic in env_karman_cloak_standard.step().
"""
# lag from middle sensor
ref = target_states[conv_len:2 * conv_len, 1]
cur = state_series[-conv_len:, 1]
lag = calc_lag(ref, cur)
sim_sum = 0.0
for i in range(6): # 6 sensor dimensions
target_seq = np.roll(target_states[:, i], -lag)[conv_len:2 * conv_len]
state_seq = state_series[-conv_len:, i]
sim_sum += calc_dtw_sim(target_seq, state_seq) / 6.0
return float(sim_sum)
# ---------------------------------------------------------------------------
# Dummy env for loading SB3 models
# ---------------------------------------------------------------------------
def create_dummy_env(s_dim: int = 12, a_dim: int = 3):
"""Return a gym.Env with correct observation/action spaces for model loading."""
import gymnasium as gym
from gymnasium import spaces
class DummyEnv(gym.Env):
def __init__(self):
super().__init__()
self.observation_space = spaces.Box(low=-1, high=1, shape=(s_dim,), dtype=np.float32)
self.action_space = spaces.Box(low=-1, high=1, shape=(a_dim,), dtype=np.float32)
def reset(self, seed=None):
return np.zeros(s_dim, dtype=np.float32), {}
def step(self, action):
return np.zeros(s_dim, dtype=np.float32), 0.0, False, False, {}
def render(self): pass
return DummyEnv()
def load_ppo_model(model_path: str, device: str = "cuda:0", s_dim: int = 12, a_dim: int = 3):
"""Load a PPO model with Sin activation."""
import torch
from torch.nn import Module
from stable_baselines3 import PPO
class Sin(Module):
def forward(self, x):
return torch.sin(x)
dummy_env = create_dummy_env(s_dim, a_dim)
model = PPO.load(model_path, env=dummy_env, device=device)
return model
# ---------------------------------------------------------------------------
# SINDy feature library – all in physical units
# ---------------------------------------------------------------------------
def build_sindy_feature_library(
n_sensor: int = 6,
n_force: int = 6,
) -> list:
"""Return list of feature names for SINDy regression (for reference).
Features are built on-the-fly in ``build_sindy_features()``.
"""
names = []
# bias
names.append("bias")
# raw sensor velocities (6 channels)
for i in range(n_sensor):
names.append(f"s{i}")
# raw forces (6 channels)
for i in range(n_force):
names.append(f"f{i}")
return names
def build_sindy_features(
sensors: np.ndarray, # (T, 6) raw sensor velocities
forces: np.ndarray, # (T, 6) raw forces
actions_prev: np.ndarray, # (T, 3) previous-step physical omegas (ch0_lag1)
include_extra: bool = True,
) -> np.ndarray:
"""Construct feature matrix Theta(t) for SINDy.
All quantities are in physical lattice units (not DRL-normalised).
Base library (always included):
bias(1), s0..s5, f0..f5
Extra features (if ``include_extra=True``, default):
sin(pi*s0), cos(pi*s0), # phase info from middle sensor u
ds0..ds5, # forward difference of sensor signals
a0_lag1, a1_lag1, a2_lag1 # previous-step action (memory)
Returns
-------
Theta : (T, n_features) array
"""
T = sensors.shape[0]
cols = []
# 1. bias
cols.append(np.ones(T, dtype=np.float64))
# 2. raw sensors (6)
for i in range(6):
cols.append(sensors[:, i].astype(np.float64))
# 3. raw forces (6)
for i in range(6):
cols.append(forces[:, i].astype(np.float64))
# 4. sin/cos of middle-sensor u-velocity (phase info)
s0 = sensors[:, 0].astype(np.float64) # sensor0_ux
cols.append(np.sin(np.pi * s0))
cols.append(np.cos(np.pi * s0))
# 5. sensor differences
for i in range(6):
diff = np.diff(sensors[:, i].astype(np.float64), prepend=sensors[0, i])
cols.append(diff)
# 6. previous action (lag-1 memory)
cols.append(actions_prev[:, 0].astype(np.float64)) # ch0
cols.append(actions_prev[:, 1].astype(np.float64)) # ch1
cols.append(actions_prev[:, 2].astype(np.float64)) # ch2
Theta = np.column_stack(cols)
return Theta
def get_sindy_feature_names(include_extra: bool = True) -> list:
"""Return list of feature names matching ``build_sindy_features()`` output."""
names = ["bias"]
for i in range(6):
names.append(f"s{i}")
for i in range(6):
names.append(f"f{i}")
if include_extra:
names += ["sin_s0", "cos_s0"]
for i in range(6):
names.append(f"ds{i}")
names += ["a0_lag1", "a1_lag1", "a2_lag1"]
return names
SINDY_DEFAULT_FEATURE_NAMES = get_sindy_feature_names(include_extra=True)
def fit_channel(
Theta: np.ndarray,
y: np.ndarray,
thresholds: list,
alpha: float = 1e-4,
max_iter: int = 25,
) -> tuple:
"""Fit a single channel (one cylinder) with STLSQ threshold grid.
Returns
-------
rows : list of dict per threshold
best : dict with best threshold entry
"""
import pysindy as ps
# Normalise features for thresholding stability
std = np.std(Theta, axis=0)
std = np.where(std < 1e-8, 1.0, std)
Theta_s = Theta / std
best = None
rows = []
for th in thresholds:
opt = ps.STLSQ(threshold=th, alpha=alpha, max_iter=max_iter)
opt.fit(Theta_s, y)
coef = np.asarray(opt.coef_, dtype=np.float64).flatten() / std
y_pred = Theta @ coef
ssr = float(np.sum((y - y_pred) ** 2))
sst = float(np.sum((y - np.mean(y)) ** 2) + 1e-12)
r2 = 1.0 - ssr / sst
mae = float(np.mean(np.abs(y - y_pred)))
nz = int(np.sum(np.abs(coef) > 1e-8))
entry = {"threshold": float(th), "nz": nz, "r2": r2, "mae": mae, "coef": coef}
rows.append(entry)
if best is None or r2 > best["r2"]:
best = entry
return rows, best
def print_control_law(feature_names: list, coef: np.ndarray, channel_label: str = "ch"):
"""Pretty-print a sparse control law."""
terms = []
for i, c in enumerate(coef):
if abs(c) > 1e-8:
terms.append(f"{c:.6f} * {feature_names[i]}")
print(f" {channel_label}: {' + '.join(terms)}")
nz = sum(1 for c in coef if abs(c) > 1e-8)
print(f" non-zero terms: {nz}")
# ---------------------------------------------------------------------------
# Physics-guided feature library (v2 — replaces raw obs features)
# ---------------------------------------------------------------------------
def compute_physical_symbols(
sensors: np.ndarray, # (T, 6) [s0_ux, s0_uy, s1_ux, s1_uy, s2_ux, s2_uy]
forces: np.ndarray, # (T, 6) [cyl0_fx, cyl0_fy, cyl1_fx, cyl1_fy, cyl2_fx, cyl2_fy]
actions_prev: np.ndarray, # (T, 3) previous-step physical omega
actions_prev2: np.ndarray, # (T, 3) omega(t-2), for delta terms
) -> dict:
"""Compute physics-guided symbols from raw CFD data.
Returns a dict of 1-D arrays (T,) keyed by symbol name.
"""
T = sensors.shape[0]
s = sensors.astype(np.float64)
f = forces.astype(np.float64)
a_prev = np.asarray(actions_prev, dtype=np.float64)
a_prev2 = np.asarray(actions_prev2, dtype=np.float64)
# -- sensor symbols ----------------------------------------------------
# streamwise
u0, u1, u2 = s[:, 0], s[:, 2], s[:, 4]
# cross-stream
v0, v1, v2 = s[:, 1], s[:, 3], s[:, 5]
sym = {}
sym["u_m"] = (u0 + u1 + u2) / 3.0 # mean wake deficit
sym["u_a"] = (u2 - u0) / 2.0 # antisymmetric (vortex street)
sym["u_c"] = u1.copy() # centreline streamwise
sym["u_curv"] = u0 - 2.0 * u1 + u2 # transverse curvature
sym["v_m"] = (v0 + v1 + v2) / 3.0 # mean cross-stream
sym["v_a"] = (v2 - v0) / 2.0 # antisymmetric cross
sym["v_c"] = v1.copy() # centre cross
sym["v_curv"] = v0 - 2.0 * v1 + v2 # cross curvature
# vortex phase encoding (keep on u_a which captures asymmetry)
sym["sin_ua"] = np.sin(np.pi * sym["u_a"])
sym["cos_ua"] = np.cos(np.pi * sym["u_a"])
# -- force symbols -----------------------------------------------------
# cylinders: [front_fx, front_fy, bottom_fx, bottom_fy, top_fx, top_fy]
fx_front, fy_front = f[:, 0], f[:, 1]
fx_bot, fy_bot = f[:, 2], f[:, 3]
fx_top, fy_top = f[:, 4], f[:, 5]
sym["Fx_tot"] = fx_front + fx_bot + fx_top
sym["Fx_rear"] = fx_bot + fx_top
sym["Fx_diff"] = fx_top - fx_bot
sym["Fy_tot"] = fy_front + fy_bot + fy_top
sym["Fy_rear"] = fy_bot + fy_top
sym["Fy_diff"] = fy_top - fy_bot
# -- memory symbols ----------------------------------------------------
sym["a0_lag1"] = a_prev[:, 0]
sym["a1_lag1"] = a_prev[:, 1]
sym["a2_lag1"] = a_prev[:, 2]
sym["da0"] = a_prev[:, 0] - a_prev2[:, 0]
sym["da1"] = a_prev[:, 1] - a_prev2[:, 1]
sym["da2"] = a_prev[:, 2] - a_prev2[:, 2]
return sym
# Which physical symbols to always include in the base library
PHYSICAL_BASE_SYMBOLS = [
"bias",
"u_m", "u_a", "u_c",
"v_a",
"Fx_tot", "Fx_rear", "Fy_tot", "Fy_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
]
# Which symbols get mu modulation
PHYSICAL_MU_SYMBOLS = [
"u_a", "v_a", "Fx_tot", "Fy_diff", "Fy_tot",
]
def build_physical_features(
sensors: np.ndarray,
forces: np.ndarray,
actions_prev: np.ndarray,
actions_prev2: np.ndarray,
mu: float = 0.0,
include_mu: bool = True,
) -> tuple:
"""Construct physics-guided feature matrix.
Returns
-------
Theta : (T, n_feat) ndarray
names : list of feature names
"""
sym = compute_physical_symbols(sensors, forces, actions_prev, actions_prev2)
T = sensors.shape[0]
cols = []
names = []
# 1. bias
cols.append(np.ones(T, dtype=np.float64))
names.append("bias")
# 2. base physical symbols
for key in PHYSICAL_BASE_SYMBOLS[1:]: # skip "bias" since we already added it
if key in sym:
cols.append(sym[key])
names.append(key)
# 3. mu and mu-modulated terms
if include_mu and mu > 0:
cols.append(np.full(T, mu, dtype=np.float64))
names.append("mu")
for key in PHYSICAL_MU_SYMBOLS:
if key in sym:
cols.append(sym[key] * mu)
names.append(f"mu_{key}")
Theta = np.column_stack(cols)
return Theta, names
def get_physical_feature_names(mu: float = 0.0, include_mu: bool = True) -> list:
"""Return feature names matching ``build_physical_features()``."""
names = ["bias"] + PHYSICAL_BASE_SYMBOLS[1:]
if include_mu and mu > 0:
names.append("mu")
for key in PHYSICAL_MU_SYMBOLS:
names.append(f"mu_{key}")
return names
# ---------------------------------------------------------------------------
# Dimensionless conversion and G operator (v3)
# ---------------------------------------------------------------------------
def compute_dimensionless(
sensors: np.ndarray, # (T, 6) raw lattice [s0_ux, s0_uy, s1_ux, s1_uy, s2_ux, s2_uy]
forces: np.ndarray, # (T, 6) raw lattice [cyl0_fx, cyl0_fy, cyl1_fx, cyl1_fy, cyl2_fx, cyl2_fy]
u0: float = 0.01,
d: float = 20.0,
rho: float = 1.0,
) -> dict:
"""Convert raw lattice CFD data to dimensionless physical quantities.
Returns dict with keys:
u_hat_B, u_hat_C, u_hat_T : nondim streamwise velocity at 3 sensors
v_hat_B, v_hat_C, v_hat_T : nondim crosswise velocity
Cd_F, Cd_T, Cd_B : drag coefficient per cylinder
Cl_F, Cl_T, Cl_B : lift coefficient per cylinder
"""
T = sensors.shape[0]
s = sensors.astype(np.float64)
f = forces.astype(np.float64)
# Sensor velocity ordering from legacy env: [sensor0_ux, sensor0_uy, sensor1_ux, sensor1_uy, sensor2_ux, sensor2_uy]
# Sensor positions: sensor0=top(y=+2L0), sensor1=mid(y=0), sensor2=bottom(y=-2L0)
# So top = sensor0 = s[:,0:2], mid = sensor1 = s[:,2:4], bottom = sensor2 = s[:,4:6]
# Per G-operator convention: B=bottom=sensor2, C=centre=sensor1, T=top=sensor0
u_hat_T = s[:, 0] / u0 # top
v_hat_T = s[:, 1] / u0
u_hat_C = s[:, 2] / u0 # centre
v_hat_C = s[:, 3] / u0
u_hat_B = s[:, 4] / u0 # bottom
v_hat_B = s[:, 5] / u0
# Force ordering: [front_fx, front_fy, bottom_fx, bottom_fy, top_fx, top_fy]
# front=cyl0, bottom=cyl1, top=cyl2
Cd_F = 2.0 * f[:, 0] / (rho * u0**2 * d)
Cl_F = 2.0 * f[:, 1] / (rho * u0**2 * d)
Cd_B = 2.0 * f[:, 2] / (rho * u0**2 * d) # bottom
Cl_B = 2.0 * f[:, 3] / (rho * u0**2 * d)
Cd_T = 2.0 * f[:, 4] / (rho * u0**2 * d) # top
Cl_T = 2.0 * f[:, 5] / (rho * u0**2 * d)
return {
"u_hat_B": u_hat_B, "u_hat_C": u_hat_C, "u_hat_T": u_hat_T,
"v_hat_B": v_hat_B, "v_hat_C": v_hat_C, "v_hat_T": v_hat_T,
"Cd_F": Cd_F, "Cd_T": Cd_T, "Cd_B": Cd_B,
"Cl_F": Cl_F, "Cl_T": Cl_T, "Cl_B": Cl_B,
}
def apply_G_x(
u_hat_B: np.ndarray, u_hat_C: np.ndarray, u_hat_T: np.ndarray,
v_hat_B: np.ndarray, v_hat_C: np.ndarray, v_hat_T: np.ndarray,
Cd_F: np.ndarray, Cd_T: np.ndarray, Cd_B: np.ndarray,
Cl_F: np.ndarray, Cl_T: np.ndarray, Cl_B: np.ndarray,
aF_lag1: np.ndarray, aT_lag1: np.ndarray, aB_lag1: np.ndarray,
daF: np.ndarray, daT: np.ndarray, daB: np.ndarray,
):
"""Apply mirror operator G to input state.
CORRECTED: all rotation-related quantities (action, lag, delta)
change sign under mirror (y -> -y).
G flips y -> -y:
- u_x: reorder only (B<->T), no sign change
- v_y: reorder (B<->T) AND sign change
- Cd: no sign change, B<->T
- Cl: sign change, B<->T
- aF: sign change
- aT <-> aB (swap AND sign change on the swapped values)
- da: same as a
Returns dict with same keys as inputs, values are G-transformed.
"""
return {
"u_hat_B": u_hat_T, "u_hat_C": u_hat_C, "u_hat_T": u_hat_B,
"v_hat_B": -v_hat_T, "v_hat_C": -v_hat_C, "v_hat_T": -v_hat_B,
"Cd_F": Cd_F, "Cd_T": Cd_B, "Cd_B": Cd_T,
"Cl_F": -Cl_F, "Cl_T": -Cl_B, "Cl_B": -Cl_T,
"aF_lag1": -aF_lag1, "aT_lag1": -aB_lag1, "aB_lag1": -aT_lag1,
"daF": -daF, "daT": -daB, "daB": -daT,
}
def apply_G_alpha(alpha: np.ndarray) -> np.ndarray:
"""Apply G to output action: [aF, aT, aB] -> [-aF, -aB, -aT]."""
return np.array([-alpha[0], -alpha[2], -alpha[1]], dtype=alpha.dtype)
# ---------------------------------------------------------------------------
# v3 physical symbols (dimensionless + equivariant-compatible)
# ---------------------------------------------------------------------------
def compute_v3_symbols(
dim: dict, # from compute_dimensionless()
actions_prev: np.ndarray, # (T, 3) physical omega(t-1)
actions_prev2: np.ndarray, # (T, 3) physical omega(t-2)
mu: float,
include_mu: bool = True,
) -> tuple:
"""Compute v3 physics-guided symbols from dimensionless quantities.
Returns
-------
Theta_front : np.ndarray (T, n_feat_front) — no bias column
Theta_top : np.ndarray (T, n_feat_top) — with bias column
names : list (common feature names, first is "bias" for top)
"""
T = actions_prev.shape[0]
# Sensor combinations (nondim)
u_B, u_C, u_T = dim["u_hat_B"], dim["u_hat_C"], dim["u_hat_T"]
v_B, v_C, v_T = dim["v_hat_B"], dim["v_hat_C"], dim["v_hat_T"]
u_m = (u_B + u_C + u_T) / 3.0
u_a = (u_T - u_B) / 2.0 # antisymmetric (T - B)
u_c = u_C.copy()
v_a = (v_T - v_B) / 2.0 # antisymmetric (T - B)
# Force combinations (dimensionless Cd/Cl)
Cd_F, Cd_T, Cd_B = dim["Cd_F"], dim["Cd_T"], dim["Cd_B"]
Cl_F, Cl_T, Cl_B = dim["Cl_F"], dim["Cl_T"], dim["Cl_B"]
Cd_tot = Cd_F + Cd_T + Cd_B
Cd_rear = Cd_T + Cd_B
Cl_tot = Cl_F + Cl_T + Cl_B
Cl_diff = Cl_T - Cl_B
# Phase
sin_ua = np.sin(np.pi * u_a)
cos_ua = np.cos(np.pi * u_a)
# Memory (all 3 cylinders now that we dropped exchange equivariance)
aF_lag1 = actions_prev[:, 0]
aB_lag1 = actions_prev[:, 1]
aT_lag1 = actions_prev[:, 2]
daF = actions_prev[:, 0] - actions_prev2[:, 0]
daB = actions_prev[:, 1] - actions_prev2[:, 1]
daT = actions_prev[:, 2] - actions_prev2[:, 2]
# Base features (common to all cylinders)
base_feats = {
"u_m": u_m, "u_a": u_a, "u_c": u_c, "v_a": v_a,
"Cd_tot": Cd_tot, "Cd_rear": Cd_rear,
"Cl_tot": Cl_tot, "Cl_diff": Cl_diff,
"sin_ua": sin_ua, "cos_ua": cos_ua,
"aF_lag1": aF_lag1, "aB_lag1": aB_lag1, "aT_lag1": aT_lag1,
"daF": daF, "daB": daB, "daT": daT,
}
# Build feature arrays
base_names = list(base_feats.keys())
cols_base = [base_feats[k] for k in base_names]
# Mu modulation
mu_names = []
if include_mu and mu > 0:
mu_feats = {
"mu": np.full(T, mu, dtype=np.float64),
"mu_u_a": u_a * mu,
"mu_v_a": v_a * mu,
"mu_Cd_tot": Cd_tot * mu,
"mu_Cl_diff": Cl_diff * mu,
}
mu_names = list(mu_feats.keys())
cols_mu = [mu_feats[k] for k in mu_names]
else:
cols_mu = []
# Front model: NO bias
Theta_front = np.column_stack(cols_base + cols_mu)
# Top model: WITH bias
cols_top = [np.ones(T, dtype=np.float64)] + cols_base + cols_mu
Theta_top = np.column_stack(cols_top)
names = ["bias"] + base_names + mu_names
return Theta_front, Theta_top, names
src/analysis_crossre/steady_cloak_plan.md
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# Steady Cloak Analysis Plan
## Objective
Extend the Karman cloak unified control framework to steady cloak, testing whether the same physical skeleton (features, symmetry, front no-bias, rear shared-head) applies across different cloak tasks.
## Data Availability Check
### Trained Models
Looking at the models directory and legacy training scripts:
| Model | Task | Status |
|-------|------|--------|
| d1a3o12_re50/re100/re200/re400 | Karman cloak | Phase 1 data exists |
| d1a3o12_250326 | Karman cloak (Re100) | Same as re100 |
| d1a3o12_250421_* | Reduced obs Karman | Different env |
| d1a3o14_250525_imit_* | Illusion | Different task |
| d1a3o12_250729_erase_* | Erase | Different task |
**No dedicated steady cloak model found in models/.**
The steady cloak results in the Confirmation report (Section 4.1) used the same d1a3o12_re100 architecture but trained from scratch with steady inflow target.
### Key Differences: Steady vs Karman Cloak
| Aspect | Karman Cloak | Steady Cloak |
|--------|-------------|--------------|
| Upstream disturbance | Cylinder (D=20) generating Karman vortex street | None (clean parabolic inflow) |
| Target signal | 3-sensor time series of undisturbed vortex street | Constant mean inlet velocity |
| Sensor output | Periodic (oscillating around mean) | Steady (near-constant) |
| PPO model | Re50-400 series | Unknown model name |
| Sample interval | 800 | Likely same |
| Action bias | [0, -4U0, +4U0] | Likely [0, 0, 0] or different |
### Feature Implications for Steady Cloak
When target is steady inflow:
- `u_a` (antisymmetric velocity) -> ~0 (symmetry at mean)
- `v_a` -> ~0
- `sin_ua`, `cos_ua` -> ~0 (no oscillation to encode)
- `Fy_tot`, `Fy_diff` -> ~0 (no lift oscillation)
- `Fx_tot`, `Fx_rear` -> non-zero (base drag)
- Memory terms -> non-zero (action smoothing still needed)
- Mu modulation -> applicable
**Many sensor-side features vanish!** This means the steady cloak likely relies mainly on force feedback + memory, not sensor feedback. This is a critical structural difference.
### Hypothesis
If the shared cloak skeleton exists, then:
1. The feature set for steady cloak should be a SUBSET of Karman cloak features
2. Force feedback + memory terms should be present in both
3. Sensor asymmetry terms (u_a, v_a, sin/cos) are Karman-specific
4. Front no-bias and rear shared-head should still apply
## Execution Steps
### Step 1: Identify/Obtain Steady Cloak Model
**Action**: Check if steady cloak model exists under a different name, or was trained as part of the d1a3o12_250326 series.
**Look for**:
- Legacy training scripts that train with steady target (no disturbance cylinder)
- In `legacy_train/` check for any script that uses `env_karman_cloak_standard.py` or similar but without the disturbance cylinder
### Step 2: Phase 1 Inference (Steady)
If model found, run Phase 1 inference for steady cloak (similar to `phase1_infer.py` but with steady target):
1. Build env WITHOUT disturbance cylinder
2. Record target: clean parabolic inflow (constant sensor values)
3. Add pinball
4. Compute norm
5. Run uncontrolled + controlled (PPO) rollout
**If no model exists**:
- Option A: Train a steady cloak PPO from scratch (1-2 days GPU time)
- Option B: Use the existing d1a3o12_re100 model with steady target - test if the Karman cloak model generalizes to steady inflow
- Option C: Use open-loop control from the report (discovered constant rotation)
Option B is fastest: test `d1a3o12_re100` with steady inflow, record performance. If similarity > 0.8, the model transfers.
### Step 3: Feature Support Analysis
Compare active features between steady and Karman cloak:
1. Fit SINDy on steady cloak data with same feature library
2. Compare support set (which features have non-zero coefficients)
3. Check G equivariance (should still hold)
4. Check front no-bias (should still hold)
5. Test rear shared-head (v23 structure)
### Step 4: Cross-task Unified Fit
If supports overlap:
1. Stack steady + Karman + (optionally) vortex data
2. Fit unified model with task-modulated parameters
3. Test in closed-loop
## Resource Requirements
| Step | Data needed | CFD inference | GPU time |
|------|------------|---------------|----------|
| 1 | None | No | ~0 |
| 2 | Steady model | Yes (~200s/Re) | ~3min |
| 3 | Steady rollout NPZ | No | ~0 |
| 4 | All NPZ data | No | ~1min |
## Risk Assessment
| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|-----------|
| No steady cloak model exists | HIGH | HIGH | Option B (transfer Karman model) |
| Steady cloak uses different obs/action scaling | MEDIUM | MEDIUM | Check norm.json before fitting |
| Sensor features vanish (u_a, v_a ~0) | CERTAIN | LOW | This is expected; force+memory should dominate |
| G equivariance test fails for steady | LOW | MEDIUM | Steady flow is symmetric by construction |
## Next Step
Begin with Step 1: find the steady cloak model. Check `d1a3o12_250326.zip` and any other models. If none found, test `d1a3o12_re100.zip` on steady inflow as Option B.
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"""
Configuration management for DynamisLab.
Handles loading of CelerisLab configurations and project settings.
"""
import os
import sys
from pathlib import Path
from typing import Optional, Tuple
# Determine project root directory
_current_file = Path(__file__).resolve()
_project_root = _current_file.parent.parent.parent # Go up to DynamisLabNew/
# Configuration directory (relative to project root)
CONFIG_DIR = _project_root / 'configs'
# Output directories
MODELS_DIR = _project_root / 'models'
OUTPUT_DIR = _project_root / 'output'
TENSORBOARD_DIR = _project_root / 'tensorboard'
# Create output directories if they don't exist
MODELS_DIR.mkdir(exist_ok=True)
OUTPUT_DIR.mkdir(exist_ok=True)
TENSORBOARD_DIR.mkdir(exist_ok=True)
def setup_celeris_import() -> None:
"""
Setup CelerisLab import path.
Assumes CelerisLab is either:
1. Installed as a package (pip install CelerisLab)
2. Available as a git submodule in project root
3. Available via PYTHONPATH environment variable
"""
try:
# Try to import CelerisLab (it might be installed)
import CelerisLab
return
except ImportError:
pass
# Check for CelerisLab as submodule
celeris_submodule = _project_root / 'CelerisLab' / 'src'
if celeris_submodule.exists():
sys.path.insert(0, str(celeris_submodule))
return
# If still not found, raise error with helpful message
raise ImportError(
"CelerisLab not found. Please either:\n"
" 1. Install it: pip install -e ../CelerisLab\n"
" 2. Add as git submodule: git submodule add <url> CelerisLab\n"
" 3. Set PYTHONPATH to include CelerisLab src directory"
)
def load_celeris_configs(
cuda_config_path: Optional[str] = None,
field_config_path: Optional[str] = None
) -> Tuple:
"""
Load CelerisLab configurations.
Args:
cuda_config_path: Optional path to CUDA config. If None, uses CONFIG_DIR.
field_config_path: Optional path to field config. If None, uses CONFIG_DIR.
Returns:
Tuple of (config_cuda, config_field)
"""
# Setup CelerisLab import
setup_celeris_import()
from CelerisLab import utils
# Set environment variable to point to our configs
os.environ['CELERISLAB_CONFIG_DIR'] = str(CONFIG_DIR)
# Load configurations - CelerisLab will find them automatically
if cuda_config_path is None:
config_cuda = utils.load_cuda_config()
else:
config_cuda = utils.load_cuda_config(cuda_config_path)
if field_config_path is None:
config_field = utils.load_flow_field_config()
else:
config_field = utils.load_flow_field_config(field_config_path)
return config_cuda, config_field
def get_model_path(model_name: str) -> Path:
"""Get full path for a model file."""
return MODELS_DIR / f"{model_name}.zip"
def get_tensorboard_logdir(run_name: str) -> Path:
"""Get TensorBoard log directory for a run."""
logdir = TENSORBOARD_DIR / run_name
logdir.mkdir(exist_ok=True)
return logdir
def get_output_path(filename: str) -> Path:
"""Get full path for an output file."""
return OUTPUT_DIR / filename
# Expose project paths for convenience
__all__ = [
'CONFIG_DIR',
'MODELS_DIR',
'OUTPUT_DIR',
'TENSORBOARD_DIR',
'setup_celeris_import',
'load_celeris_configs',
'get_model_path',
'get_tensorboard_logdir',
'get_output_path',
]
src/drl_pinball/__init__.py
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src/drl_pinball/knowledge.md
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# DynamisLab Comprehensive Knowledge Document
> **Project**: Active hydrodynamic cloaking and illusion using Deep Reinforcement Learning (DRL) on a fluidic pinball.
> **Solver**: GPU-accelerated Lattice Boltzmann Method (LBM, D2Q9, MRT)
> **DRL**: PPO with Sin activation, Stable-Baselines3
---
## 1. Grid Configuration
### 1.1 Legacy Grid
Two config files are multiplied to produce the actual lattice:
| Config | Base (1U) | Multiplier (field_dim_in_U) | Result |
|--------|-----------|----------------------------|--------|
| `config_cuda.json` | X_1U=128, Y_1U=32, Z_1U=1 | — | — |
| `config_flowfield.json` | — | field_dim_in_U=[10, 16, 1] | — |
| **Actual grid** | — | — | **nx=1280, ny=512, nz=1** |
`L0=20` is the base length unit. The "U" grid units (128, 32) are chosen to align with CUDA SM count on the GPU.
### 1.2 New Grid (CelerisLab)
| Config File | nx | ny | nz |
|------------|----|----|-----|
| `config_lbm_pinball.json` | 1280 | 512 | 1 |
The new grid is **identical** to the legacy grid. `L0=20` remains the base length unit.
### 1.3 Config File Locations
- Legacy configs: `configs/legacy_configs/config_cuda.json`, `config_flowfield.json`
- New config: `configs/config_lbm_pinball.json`
- Body config: `configs/config_body.json`
- Reference: `configs/CONFIG.md` (full config schema documentation)
---
## 2. Reynolds Number Definition
**Critical**: The project uses two different Re definitions.
| Symbol | Reference Length | Formula | Default Value |
|--------|-----------------|---------|---------------|
| Re_D (report/paper) | Single cylinder diameter D=20 | U0·D/ν | 0.01×20/0.004 = **50** |
| Re (code) | 2×D = 40 | U0·(2D)/ν | 0.01×40/0.004 = **100** |
**Key mappings**:
- Confirmation report `Re_D = 50` ↔ code `Re=100`
- Code `re100` models → physical `Re_D=50`
- Code `re50` models → physical `Re_D=25`
- Upstream disturbance cylinder (diameter = L0×1 = 20) → Re=U0×20/ν=50 (single diameter definition)
### 2.1 Re via Viscosity
| Code Name | Viscosity ν | Re (code, 2D ref) | Re_D (report) |
|-----------|-------------|-------------------|---------------|
| re50 | 0.008 | 50 | 25 |
| re100 | 0.004 | 100 | 50 |
| re200 | 0.002 | 200 | 100 |
| re400 | 0.001 | 400 | 200 |
Formula: `Re = U0 * ref_length / ν` where ref_length = 2D = 40 for code Re.
---
## 3. Boundary Conditions
Both legacy and new simulations use the same boundary configuration:
| Boundary | Condition | Details |
|----------|-----------|---------|
| Inlet (x=0) | **Parabolic profile**, Zou-He local scheme | u_x parabolic, u_y=0 |
| Outlet (x=64D) | Convective / NEQ extrapolation | `neq_extrap` mode, backflow clamp enabled |
| Top/Bottom walls (y=±12.8D) | **Bounce-back** (no-slip) | `y_wall_bc: "bounce_back"` |
| Cylinder surfaces | Ghost-node interpolation with prescribed rotational velocity | u_wall = a·(-sinθ, cosθ)^T |
**Note**: The `config_lbm_pinball.json` explicitly uses `y_wall_bc: "bounce_back"`, which is equivalent to the legacy no-slip walls. The validation config `run_kan99b` uses `free_slip`, so be careful to use the correct config.
**Fixed parameters**:
- U0 = 0.01 (inlet center velocity, lattice units)
- ν = 0.004 (default for Re=100 code)
- ρ = 1.0
- Collision: MRT
- Streaming: esopull
- Data type: FP32
---
## 4. Complete Old-to-New API Conversion Table
| Feature | Old API (FlowField, LegacyCelerisLab) | New API (Simulation, CelerisLab) |
|---------|--------------------------------------|----------------------------------|
| **Force reading** | `obs[i]` after `run()` = per-step average (internal ÷N) | `read_force(id)` = N-step cumulative sum; **must divide by N** |
| **Sensor reading** | `obs[i]` after `run()` = per-step average (internal ÷N) | `read_sensor(id, normalize=True)` = raw sum ÷ cell_count; **must still divide by N** |
| **Action setting** | `run(N, action_array)` — objects indexed by order in single array | `set_body(id, omega=value)` — each cylinder set separately |
| **Action smoothing** | Built-in exponential smoothing (weight=0.1) | None — implement manual `ActionSmoother` if needed |
| **Checkpoint/save** | `save_ddf()` / `restore_ddf()` / `apply_ddf()` (host memory) | `snapshot()` / `restore()` (memory) or `save_checkpoint(path)` / `load_checkpoint(path)` (HDF5) |
| **Initialization** | Constructor `FlowField(config_field, config_cuda, device_id)` auto-initializes | `Simulation(config)` then call `initialize()` separately |
| **Field output** | `save_field()` writes Tecplot `.dat` | `get_macroscopic()` returns numpy arrays |
| **Object addition** | `add_cylinder()`, `add_sensor()` on FlowField | Objects defined in `config_body.json` or added before `initialize()` |
| **Vortex addition** | `add_vortex(center, radius, strength, ...)` | Unknown — check API |
| **Numeric error check** | `flow_field.has_numeric_error()`, `flow_field.last_error_flag` | Manual implementation needed |
| **Context management** | `flow_field.context.push()` / `.pop()` | Stream management via API |
### 4.1 Conversion Formulas
```python
# Old API (per-step average):
flow_field.run(SAMPLE_INTERVAL, action_array)
obs = flow_field.obs # already per-step average
# New API (must divide by N):
sim.bodies.zero_force_segment_async(stream)
sim.bodies.zero_sensor_segment_async(stream)
sim.run(SAMPLE_INTERVAL)
fx_per_step = sim.read_force(body_id)[0] / SAMPLE_INTERVAL
fy_per_step = sim.read_force(body_id)[1] / SAMPLE_INTERVAL
ux_per_step = sim.read_sensor(sensor_id)[0] / SAMPLE_INTERVAL
uy_per_step = sim.read_sensor(sensor_id)[1] / SAMPLE_INTERVAL
```
---
## 5. Per-Scene Geometry Map
All coordinates in `L0=20` lattice units. Multiply by `L0` to get lattice coordinates unless otherwise noted.
- `CENTER_Y = (NY-1)/2 = 255.5` (lattice units)
- `NY = 512`, `NX = 1280`
### 5.1 Karman Cloak / Erase / ReducedObs (Standard Pinball Layout)
| Object | Position (L0 units) | Position (lattice) | Radius (L0) | Radius (lattice) |
|--------|---------------------|--------------------|-------------|------------------|
| Disturbance cylinder (upstream) | (10, CENTER_Y/L0, 0) | (200, 255.5, 0) | 1.0×L0 | 20 |
| Sensors (3x) | x=40, y=CENTER_Y/L0 + [2, 0, -2] | x=800 | L0/4 | 5 |
| Pinball front | (30, CENTER_Y/L0, 0) | (600, 255.5, 0) | L0/2 | 10 |
| Pinball bottom | (31.3, CENTER_Y/L0 − 0.75, 0) | (626, 240.5, 0) | L0/2 | 10 |
| Pinball top | (31.3, CENTER_Y/L0 + 0.75, 0) | (626, 270.5, 0) | L0/2 | 10 |
**Object order in legacy API** (for cloak/erase/reduce_obs):
1. sensor0 (top, y=CENTER_Y+2*L0)
2. sensor1 (center, y=CENTER_Y)
3. sensor2 (bottom, y=CENTER_Y-2*L0)
4. dist_cylinder (upstream disturbance)
5. pinball_front
6. pinball_bottom
7. pinball_top
### 5.2 Illusion (Imit) Layout
**Target cylinder (recorded separately)**:
| Object | Position (L0 units) | Radius |
|--------|---------------------|--------|
| Target cylinder | (20, CENTER_Y/L0, 0) | [0.75, 1.0, 1.5]×L0 (varies) |
| Sensors (3x) | x=30, y=CENTER_Y/L0 + [2, 0, -2] | L0/4 |
**Pinball + sensors** (trained env):
| Object | Position (L0 units) | Radius |
|--------|---------------------|--------|
| Sensors (3x) | x=30, y=CENTER_Y/L0 + [2, 0, -2] | L0/4 |
| Pinball front | (19, CENTER_Y/L0, 0) | L0/2 |
| Pinball bottom | (20.3, CENTER_Y/L0 + 0.75, 0) | L0/2 |
| Pinball top | (20.3, CENTER_Y/L0 − 0.75, 0) | L0/2 |
**Object order** (illusion, 6 objects — no disturbance cylinder):
1. sensor0 (top, y=CENTER_Y+2*L0)
2. sensor1 (center, y=CENTER_Y)
3. sensor2 (bottom, y=CENTER_Y-2*L0)
4. pinball_front
5. pinball_bottom
6. pinball_top
Action array: `temp[3:6] = (action*8 + [0, -2, 2]) * U0`
### 5.3 Vortex Layout
**Target phase** (sensors + vortex, no pinball):
| Object | Position (L0 units) | Radius |
|--------|---------------------|--------|
| Sensors (3x) | x=40, y=CENTER_Y/L0 + [2, 0, -2] | L0/4 |
| Vortex | (10, CENTER_Y/L0, 0) | 2×L0 |
**Pinball phase** (sensors + pinball + vortex):
| Object | Position (L0 units) | Radius |
|--------|---------------------|--------|
| Sensors (3x) | x=40 | L0/4 |
| Pinball front | (30, CENTER_Y/L0, 0) | L0/2 |
| Pinball bottom | (31.3, CENTER_Y/L0 + 0.75, 0) | L0/2 |
| Pinball top | (31.3, CENTER_Y/L0 − 0.75, 0) | L0/2 |
| Vortex | (15, CENTER_Y/L0, 0) | 2×L0 |
**Vortex types**:
- **Lamb dipole**: strength=0.5×U0, type="lamb"
- **Taylor monopole**: strength=0.03×U0, type="taylor"
**MAX_STEPS = 150** (transient event — not infinite like other scenes)
**Object order** (vortex, 6 objects — no disturbance cylinder):
1. sensor0 (top)
2. sensor1 (center)
3. sensor2 (bottom)
4. pinball_front
5. pinball_bottom
6. pinball_top
Action array: `temp[3:6] = (action*4 + [0, -4, 4]) * U0`
---
## 6. Per-Scene Action Scaling
Each scene maps the normalized DRL action (range [-1, 1]) to physical angular velocity ω (U0 multiples):
| Scene | Formula (ω/U0) | Scale | Bias | Physical range [front, bottom, top] |
|-------|---------------|-------|------|-------------------------------------|
| **Cloak (Karman)** | `action×8 + [0, -4, 4]` | 8 | [0, -4, 4] | front: [-8,8], bottom: [-12,4], top: [-4,12] |
| **Erase** | `action×8 + [0, -8, 8]` | 8 | [0, -8, 8] | front: [-8,8], bottom: [-16,0], top: [0,16] |
| **Illusion (Imit)** | `action×8 + [0, -2, 2]` | 8 | [0, -2, 2] | front: [-8,8], bottom: [-10,6], top: [-6,10] |
| **Vortex** | `action×4 + [0, -4, 4]` | 4 | [0, -4, 4] | front: [-4,4], bottom: [-8,0], top: [0,8] |
**Final omega in lattice units**: Multiply the result by `U0=0.01`.
**Example** (Cloak, action=[1, 1, 1]):
```
ω_front = (1*8 + 0) * 0.01 = 0.08
ω_bottom = (1*8 + (-4)) * 0.01 = 0.04
ω_top = (1*8 + 4) * 0.01 = 0.12
```
**Example** (Cloak, action=[0, 0, 0] — the bias actions):
```
ω_front = 0
ω_bottom = -4 * 0.01 = -0.04
ω_top = 4 * 0.01 = 0.04
```
### 6.1 Action Smoothing (Legacy Only)
Legacy `FlowField.run()` has **built-in exponential smoothing**:
```python
action_pinned = (1 - weight) * action_pinned + weight * action_target
# weight = 0.1
```
This means the actual applied ω smoothly transitions toward the target. The new API has **no built-in smoothing** — implement `ActionSmoother` manually if needed for numerical stability.
---
## 7. Norm Semantics
The normalization values are computed during environment initialization and **must be identical during inference**. They are model-specific and cannot be reused across different scenarios.
### 7.1 Norm Collection Procedure (Standard Pattern)
```python
# Phase 1: Zero-action rollout
for i in range(FIFO_LEN):
flow_field.run(SAMPLE_INTERVAL, zero_action) # 4 or 7 objects depending on phase
fifo_states.append(flow_field.obs[sensor_select]) # e.g. [2:14] skips dist_cyl
# Phase 2: Compute normalization factors
temp_states = np.array(fifo_states) # shape: (FIFO_LEN, N_sensors+N_forces)
force_norm_fact = 6 * max(|forces|) # forces = temp_states[:, 6:12]
for i in range(6):
sens_deviation[i] = mean(sensor_i) # sensor_i = temp_states[:, i]
sens_norm_fact[i] = 5 * max(|sensor_i - mean|)
# Phase 3: Bias-action rollout (for FIFO initialization)
flow_field.apply_ddf() # restore checkpoint
for i in range(FIFO_LEN):
flow_field.run(SAMPLE_INTERVAL, bias_action)
fifo_states.append(...)
save_states = fifo_states.copy()
```
### 7.2 Norm Format
```python
norm = {
"force_norm_fact": float, # scalar = 6 * max(|forces|)
"sens_deviation": [6 floats], # mean per sensor channel
"sens_norm_fact": [6 floats], # 5 * max(|sensor - deviation|) per channel
"save_states": ndarray, # FIFO_LEN × N_obs array after bias rollout
"action_bias": [b_front, b_bottom, b_top], # e.g. [0.0, -4.0, 4.0]
"n_obj_total": int # total objects in flow field
}
```
### 7.3 Scene-Specific Norm Variations
| Scene | force_norm_fact formula | sens_norm_fact factor | Obs slice (from fifo) |
|-------|------------------------|----------------------|----------------------|
| Cloak (standard) | `6 * max(\|forces\|)` | 5 | `obs[2:14]` (skip 2 dist_cyl sensor channels) |
| Erase | `100 * max(\|forces\|)` | 10 | `obs[0:14]` (full 14, incl. dist force) |
| Illusion | `6 * max(\|forces\|)` | 5 | `obs[0:12]` (full 12) |
| Vortex | `6 * max(\|forces\|)` | 5 | `obs[0:12]` (full 12) |
| ReducedObs | `10 * max(\|forces\|)` | 5 | `obs[2:14]` |
### 7.4 Observation Normalization (per step)
```python
# cloak/standard:
forces = obs_slice[6:12] / force_norm_fact
sens = (obs_slice[0:6] - sens_deviation) / sens_norm_fact
observation = clip(hstack([forces, sens]), -1, 1)
# erase:
forces = obs_slice[6:14] / force_norm_fact # Note: 8 force values (includes dist_cylinder)
# But only forces[2:8] (pinball forces) are used in observation
sens = (obs_slice[0:6] - sens_deviation) / sens_norm_fact
```
### 7.5 L0 Units vs Lattice Coordinates
Multiple sources define positions in L0 units; multiply by L0=20 to get lattice (pixel) coordinates.
| Description | L0 units | Lattice (pixels) |
|-------------|----------|-------------------|
| Grid size | — | 1280 × 512 |
| Center Y (CENTER_Y) | — | (512-1)/2 = 255.5 |
| Disturbance cylinder x | 10×L0 → 10 | 200 |
| Sensor x (cloak/erase/vortex/reduce) | 40×L0 → 40 | 800 |
| Sensor x (illusion) | 30×L0 → 30 | 600 |
| Pinball front x (cloak/erase/vortex/reduce) | 30×L0 | 600 |
| Pinball front x (illusion) | 19×L0 | 380 |
| Pinball bottom/top x (cloak/erase/vortex/reduce) | 31.3×L0 | 626 |
| Pinball bottom/top x (illusion) | 20.3×L0 | 406 |
| Pinball radius (all) | L0/2 | 10 |
| Sensor radius (all) | L0/4 | 5 |
| Disturbance cylinder radius (cloak/erase) | L0 | 20 |
| Vortex radius | 2×L0 | 40 |
| Target cylinder radius (illusion) | [0.75, 1.0, 1.5]×L0 | [15, 20, 30] |
---
## 8. Per-Scene Running Parameters
| Parameter | Cloak | Erase | Illusion | Vortex | ReducedObs |
|-----------|-------|-------|----------|--------|------------|
| S_DIM | 12 | 12 | 14 | 12 | varies (3→2) |
| A_DIM | 3 | 3 | 3 | 3 | 3 |
| SAMPLE_INTERVAL | 800 | 600 | varies | 800 | 800 |
| FIFO_LEN | 150 | 150 | 150 | 150 | 150 |
| CONV_LEN | 30 | 36 | 36 | 30 | 36 |
| MAX_STEPS | 500 | 500 | 500 | **150** | 500 |
| T0 | 1000 | 1000 | 1000 | 1000 | 1000 |
| Objects in env | 7 | 7 | 6 | 6 | 7 |
| Has disturbance cyl | Yes | Yes | No | No | Yes |
| DRL training initial model | — (scratch) | `d1a3o12_250326_erase` | — (scratch) | `d1a3o12_re100` | — (scratch) |
### 8.1 Reward Functions
**Cloak (Karman)**:
```python
reward_cd = exp(-|cd * 20|) # cd = (Σforces_fx) / 3
reward_cl = exp(-|cl * 80|) # cl = (Σforces_fy) / 3
reward_sim = exp(-10 * |sim - 1|) # sim = DTW-based similarity
reward = min(0.3*reward_cd + 0.4*reward_cl + 0.3*reward_sim, 1.0)
```
**Erase**:
```python
# Target = clean inflow mean (steady), not the noisy vortex street
reward_u = exp(-|diff_u * 40|) # diff of current vs target sensor u
reward_v = 0.7*exp(-|amp_v*20|) + 0.3*exp(-|diff_v*20|)
reward_sim = similarities # raw DTW similarity (not exponentiated)
reward = min(0.4*reward_u + 0.4*reward_v + 0.2*reward_sim, 1.0)
```
**Illusion**:
```python
# Target forces from harmonics reconstruction of target cylinder
reward_cd = exp(-|(cd - cd_target) * 10|)
reward_cl = exp(-|(cl - cl_target) * 10|)
reward_sim = exp(-10 * |sim - 1|)
reward = min(0.3*reward_cd + 0.3*reward_cl + 0.4*reward_sim, 1.0)
```
**Vortex**:
```python
reward_cd = exp(-|cd * 20|)
reward_cl = exp(-|cl * 80|)
reward_sim = exp(-10 * |sim - 1|)
reward = min(0.2*reward_cd + 0.3*reward_cl + 0.5*reward_sim, 1.0)
```
---
## 9. Old API Obs Layout
### 9.1 Cloak (Karman) / Standard Env — 7 Objects
Object addition order:
1. Disturbance cylinder (id=0)
2. Sensor0 / top (id=1)
3. Sensor1 / center (id=2)
4. Sensor2 / bottom (id=3)
5. Pinball front (id=4)
6. Pinball bottom (id=5)
7. Pinball top (id=6)
**`flow_field.obs` array** (14 values = 7 objects × 2):
```
obs[0:2] = dist_cylinder force (fx, fy) — ignored in training
obs[2:4] = sensor0 velocity (ux, uy)
obs[4:6] = sensor1 velocity (ux, uy)
obs[6:8] = sensor2 velocity (ux, uy)
obs[8:10] = front_pinball force (fx, fy)
obs[10:12] = bottom_pinball force (fx, fy)
obs[12:14] = top_pinball force (fx, fy)
```
**Normalized observation** (after `obs[2:14]` slice):
```
obs_norm[0:6] = sensor0_ux, sensor0_uy, sensor1_ux, sensor1_uy, sensor2_ux, sensor2_uy
obs_norm[6:12] = front_fx, front_fy, bottom_fx, bottom_fy, top_fx, top_fy
```
**Action array** (7 entries, n_objects=7):
```
temp[0:4] = 0 (sensors + dist_cylinder — ignored)
temp[4] = front omega
temp[5] = bottom omega
temp[6] = top omega
```
### 9.2 Erase Env — 7 Objects
Same addition order as Cloak (disturbance cylinder radius=0.75*L0 instead of 1.0*L0).
**`flow_field.obs` array** (14 values):
```
obs[0:2] = dist_cylinder force (fx, fy)
obs[2:4] = sensor0 velocity (ux, uy)
obs[4:6] = sensor1 velocity (ux, uy)
obs[6:8] = sensor2 velocity (ux, uy)
obs[8:10] = front_pinball force (fx, fy)
obs[10:12] = bottom_pinball force (fx, fy)
obs[12:14] = top_pinball force (fx, fy)
```
**Normalized observation** (full `obs[0:14]` slice — include dist_cylinder forces):
```
forces = obs[6:14] / force_norm_fact # 8 force values
# But only forces[2:8] (pinball) used in step()
sens = (obs[0:6] - sens_deviation) / sens_norm_fact
```
**Target recording** uses `obs[0:6]` (sensor only, no dist cylinder force), since erase has **no disturbance cylinder during target phase**.
### 9.3 Illusion (Imit) Env — 6 Objects
Object addition order (sensors first, then pinball):
1. Sensor0 / top (id=0)
2. Sensor1 / center (id=1)
3. Sensor2 / bottom (id=2)
4. Pinball front (id=3)
5. Pinball bottom (id=4)
6. Pinball top (id=5)
**`flow_field.obs` array** (12 values):
```
obs[0:2] = sensor0 velocity (ux, uy)
obs[2:4] = sensor1 velocity (ux, uy)
obs[4:6] = sensor2 velocity (ux, uy)
obs[6:8] = front_pinball force (fx, fy)
obs[8:10] = bottom_pinball force (fx, fy)
obs[10:12] = top_pinball force (fx, fy)
```
**Normalized observation** (full `obs[0:12]`):
```
forces = obs[6:12] / force_norm_fact
sens = (obs[0:6] - sens_deviation) / sens_norm_fact
obs_norm = hstack([forces, sens]) # 12 values
# Plus 2 additional: target_cd, target_cl → total 14 (S_DIM=14)
```
**Action array** (6 entries):
```
temp[0:3] = 0 (sensors — ignored)
temp[3] = front omega
temp[4] = bottom omega
temp[5] = top omega
```
**Target recording** uses `obs[0:8]` (3 sensor × 2 + 1 cylinder × 2 = 8 values from target cylinder + 3 sensors).
### 9.4 Vortex Env — 6 Objects
Same object order as Illusion (sensors + pinball, no disturbance cylinder).
Same obs layout as Illusion (12 values, obs[0:12] used as-is).
**Target recording**: In the target phase, `obs` has 3 sensors only (6 values, `obs[0:6]`).
### 9.5 ReducedObs Env — 7 Objects
Same geometry and object order as Karman Cloak (7 objects: dist_cyl + 3 sensors + 3 pinball).
**Obs slice**: `obs[2:14]` (same as Cloak, skipping dist_cylinder forces).
**Additional torque observation**: Some reduced-obs models also compute torque:
```python
obs_torque = (-obs[1] - obs[2]*√3/2 + obs[3]/2 + obs[4]*√3/2 + obs[5]/2) / torque_norm_fact
```
The observation layout varies by model name suffix:
| Model name | S_DIM | Observation components |
|-----------|-------|----------------------|
| `forces02` | 3 | `[obs_torque, dist_fx, dist_fy]` (?) |
| `total_force` | 3 | Total force components |
| `torque+forces02` | 5 | torque + dist forces |
| `torque+forces02+sens24` | 9 | torque + dist forces + sensors 2,4 |
| `torque+forces04+sens04` | 5 | torque + forces + sensors |
See `legacy_env_reduce_obs.py` line 191 for exact obs composition.
---
## 10. Complete Model Inventory
All model `.zip` files are PPO policies with Sin activation and 64×64 hidden layers.
### 10.1 `models/old/` — Original Training (Cloak, Various Re)
| File | Env | S_DIM | A_DIM | Action Scale/Bias | Sample Interval | Re (code) | Description |
|------|-----|-------|-------|-------------------|-----------------|-----------|-------------|
| `d1a3o12_re50.zip` | Cloak | 12 | 3 | 8 / [0,-4,4] | 800 | 50 | Cloak at lower Re; base model |
| `d1a3o12_re100.zip` | Cloak | 12 | 3 | 8 / [0,-4,4] | 800 | 100 | Cloak at Re=100 (ν=0.004); **most used base model** |
| `d1a3o12_re200.zip` | Cloak | 12 | 3 | 8 / [0,-4,4] | 800 | 200 | Cloak at higher Re |
| `d1a3o12_re400.zip` | Cloak | 12 | 3 | 8 / [0,-4,4] | 800 | 400 | Cloak at highest Re |
| `vortex_lamb.zip` | Vortex | 12 | 3 | 4 / [0,-4,4] | 800 | 100 | Cloak of Lamb dipole vortex; **transfer** from re100 |
| `vortex_taylor.zip` | Vortex | 12 | 3 | 4 / [0,-4,4] | 800 | 100 | Cloak of Taylor monopole vortex; **transfer** from re100 |
**Naming convention** `d1a3o12`:
- `d1` = 1 disturbance cylinder
- `a3` = 3 actuators (pinball cylinders)
- `o12` = 12 observations
- `o14` = 14 observations (used for illusion, includes target forces)
### 10.2 `models/250326/` — Re-trained Cloak
| File | Env | S_DIM | A_DIM | Scale/Bias | Desc |
|------|-----|-------|-------|------------|------|
| `d1a3o12_250326.zip` | Cloak | 12 | 3 | 8 / [0,-4,4] | Re-trained from scratch; equivalent to re100 |
### 10.3 `models/250329/` — No-offset Cloak
| File | Env | S_DIM | A_DIM | Scale/Bias | Desc |
|------|-----|-------|-------|------------|------|
| `d0a3o12_250329_nooffset.zip` | Cloak | 12 | 3 | 8 / [0,0,0] | Disturbance-cylinder-free (d0), zero bias actions |
### 10.4 `models/250421/` — Reduced Observation
| File | Env | S_DIM | A_DIM | Scale/Bias | Desc |
|------|-----|-------|-------|------------|------|
| `d1a3o12_250421_forces02.zip` | ReducedObs | 3 | 3 | 8 / [0,-4,4] | Obs reduced to 3 values |
| `d1a3o12_250421_torque+forces02.zip` | ReducedObs | 5 | 3 | 8 / [0,-4,4] | Torque + 2 force values |
| `d1a3o12_250421_torque+forces02+sens24.zip` | ReducedObs | 9 | 3 | 8 / [0,-4,4] | Torque + forces + sensors |
| `d1a3o12_250421_torque+forces04+sens04.zip` | ReducedObs | 5 | 3 | 8 / [0,-4,4] | Modified obs composition |
| `d1a3o12_250421_torque+total_force.zip` | ReducedObs | 5 | 3 | 8 / [0,-4,4] | Torque + total force |
| `d1a3o12_250421_total_force.zip` | ReducedObs | 3 | 3 | 8 / [0,-4,4] | Only total force |
All trained from scratch (no transfer). Obs reduction experiments for experimental hardware simplification.
### 10.5 `models/250525/` — Illusion (Imit)
| File | Env | S_DIM | A_DIM | Scale/Bias | Target | Sample Interval |
|------|-----|-------|-------|------------|--------|-----------------|
| `d1a3o12_250525_imit_075L_1U.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 0.75L cylinder, U0=0.01 | 600 |
| `d1a3o12_250525_imit_1L_1U.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder, U0=0.01 | 600 |
| `d1a3o12_250525_imit_1L_1U_trans.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder, U0=0.01 | 600 (transfer) |
| `d1a3o14_250525_imit_075L_2U.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 0.75L cylinder, 2×U0 | 600 |
| `d1a3o14_250525_imit_075L_2U_1.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 0.75L cylinder | ~600 |
| `d1a3o14_250525_imit_075L_2U_400S.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 0.75L cylinder | 400 |
| `d1a3o14_250525_imit_15L_2U.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.5L cylinder, 2×U0 | 600 |
| `d1a3o14_250525_imit_1L_2U.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder, 2×U0 | 600 |
| `d1a3o14_250525_imit_1L_2U_1.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder | ~600 |
| `d1a3o14_250525_imit_1L_2U_400S_02Vis.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder | 400 |
| `d1a3o14_250525_imit_1L_2U_600S.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder | 600 |
| `d1a3o14_250525_imit_1L_2U_800S_08Vis.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder | 800 |
| `d1a3o14_250525_imit_1L_2U_1000S_08Vis.zip` | Illusion | 14 | 3 | 8 / [0,-2,2] | 1.0L cylinder | 1000 |
**Naming conventions**:
- `075L` = target cylinder diameter = 0.75×L0
- `1L` = target cylinder diameter = 1.0×L0
- `15L` = target cylinder diameter = 1.5×L0
- `1U` = U0=0.01 (standard), `2U` = 2×U0=0.02
- `400S` etc. = SAMPLE_INTERVAL
- `02Vis` etc. = ν (viscosity) multiplier (e.g. 0.08×ν)
- `trans` = transfer learning model
- `_1` suffix = variant
### 10.6 `models/250729/` — Erase & Re-cloak
| File | Env | S_DIM | A_DIM | Scale/Bias | Base Model | Desc |
|------|-----|-------|-------|------------|------------|------|
| `d1a3o12_250729_250326_cloak_800S_02Vis.zip` | Cloak | 12 | 3 | 8 / [0,-4,4] | `d1a3o12_250326` | Re-cloak, 02×ν |
| `d1a3o12_250729_250326_erase.zip` | Erase | 12 | 3 | 8 / [0,-8,8] | `d1a3o12_250326` | Erase, transfer from cloak |
| `d1a3o12_250729_250326_erase_250804_20D_retrain2.zip` | Erase | 12 | 3 | 8 / [0,-8,8] | `erase` | Erase retrain, 20D delay |
| `d1a3o12_250729_250326_erase_250804_20D_retrain3.zip` | Erase | 12 | 3 | 8 / [0,-8,8] | `erase` | Erase retrain v3 |
Erase models: SAMPLE_INTERVAL=600, CONV_LEN=36 (vs 800/30 for cloak).
---
## 11. DRL Hyperparameters
| Parameter | Value |
|-----------|-------|
| Algorithm | PPO (Stable-Baselines3 `PPO`) |
| Policy network | `MlpPolicy` |
| Hidden layers | 64 × 64 (both actor and critic) |
| Activation function | **Sin** (custom `torch.nn.Module`) |
| Optimizer | Adam |
| Learning rate (actor) | 3×10⁻⁴ |
| Learning rate (critic) | 4×10⁻⁴ |
| Episode length | 600 T₀ (T₀ = D/U₀ = 2000 LBM steps) |
| Action interval | 0.8 T₀ (one action per 0.8 flow-through times) |
| Training timesteps/iteration | 360-400 (varies by scene) |
| Total episodes | ~500 (varies; vortex uses ~100) |
| Device | CUDA GPU (device_id varies) |
| Deterministic inference | Yes (`use_deterministic=True`) |
### 11.1 Custom Sin Activation
```python
class Sin(Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(x)
```
Used in place of Tanh/ReLU because trigonometric functions better preserve spectral fidelity for vortex-dominated flows. Networks are:
```
Input(s_t) → Linear(64) → Sin → Linear(64) → Sin → Linear(n_actions) → Output(a_t)
```
### 11.2 Time Scales
| Quantity | LBM steps | Description |
|----------|-----------|-------------|
| T₀ = D/U₀ | 20/0.01 = 2000 | One flow-through time (single cylinder diameter) |
| SAMPLE_INTERVAL | 600-800 | Steps between DRL actions |
| Action interval | 0.8 T₀ | SAMPLE_INTERVAL / T₀ = 800/2000 = 0.4 ... actually 800/2000=0.4 |
| Episode length | 600 T₀ = 1.2M steps | Total episode duration |
**Correction**: The paper says "actuations per T₀ = 1.25" and "action interval = 0.8 T₀". This means SAMPLE_INTERVAL = 0.8×2000 = 1600 LBM steps? But the code says SAMPLE_INTERVAL=800. This is a discrepancy — note that the paper uses T₀ = D/U₀ and SAMPLE_INTERVAL=800, which gives 800/2000 = 0.4 T₀ = 2.5 actuations per T₀. The paper may use a different T₀ definition, or the hyperparameter table may be aspirational vs actual.
---
## 12. Target Signal & Illusion Harmonics
### 12.1 Karman Cloak Target
Recorded from: 1 disturbance cylinder + 3 sensors (no pinball).
- 150 steps × SAMPLE_INTERVAL = 800 steps
- `obs[2:8]` → 6 sensor channels stored
- Stored in `target_states` (150, 6)
### 12.2 Erase Target
Recorded from: 3 sensors only (no disturbance cylinder).
- 150 steps × SAMPLE_INTERVAL = 600 steps
- `obs[0:6]` → 6 sensor channels stored
- No periodic signal → target is mean only (clean inflow)
### 12.3 Illusion Target + Harmonics
Recorded from: 1 target cylinder + 3 sensors (no pinball).
- 150 steps × SAMPLE_INTERVAL (varies)
- `obs[0:8]` → 3 sensor (6) + 1 cylinder force (2) = 8 channels stored
- **Harmonics analysis**: FFT over target_states extracts DC + top 5 frequency harmonics per channel
- Stored as `target_harmonics`: list of dicts with `{dc, amps, freqs, phases}` per channel (8 channels total)
- During training, target forces are reconstructed via `gen_target_states_at(step, harmonics)`
### 12.4 Vortex Target
Recorded from: 3 sensors + Lamb dipole or Taylor monopole (no pinball).
- 150 steps × SAMPLE_INTERVAL = 800 steps
- `obs[0:6]` → 6 sensor channels stored
- Vortex is transient: target_states contains the evolving vortex signal
---
## 13. Training Strategies
### 13.1 Training Loop Pattern (All Scenes)
```python
for i in range(total_episodes): # 100-500
model.learn(total_timesteps=K) # K = 360-1500
test_env = model.get_env()
test_obs = test_env.reset()
for step in range(eval_steps): # 150-360
test_action, _ = model.predict(test_obs)
test_obs, reward, done, info = test_env.step(test_action)
list_reward.append(reward)
avg_reward = mean(list_reward[-tail:]) # last 100-180 steps
# Save if best
if avg_reward > max_reward:
model.save(...)
```
### 13.2 Transfer Learning
| Source Model | Target Model | Method |
|-------------|-------------|--------|
| `d1a3o12_re100` (Cloak) | `vortex_lamb`, `vortex_taylor` | Transfer: loaded as base, retrained on vortex env |
| `d1a3o12_250326` (Cloak) | Erase models | Transfer: loaded as base, retrained on erase env |
| Erase base | Erase retrain models | Transfer: loaded from previously trained erase |
| `d1a3o12_250525_imit_1L_2U_600S` | `..._1000S_08Vis` etc. | Transfer: varied SAMPLE_INTERVAL/viscosity |
Base models are loaded via `PPO.load(path, env=new_env, device=...)` and then fine-tuned.
### 13.3 Checkpoint & Reset Mechanism
```python
# During __init__:
flow_field.run(...) # stabilize
flow_field.get_ddf() # host ← GPU
flow_field.save_ddf() # save to host memory
# During reset/restore:
flow_field.restore_ddf() # restore from host memory
flow_field.apply_ddf() # host → GPU
```
The restored state includes the stable pinball wake (with vortex for vortex scenes). The FIFO is re-initialized from `save_states`.
---
## 14. File Structure Reference
```
DynamisLab/
├── configs/
│ ├── config_lbm_pinball.json # NEW LBM config (nx=1280, ny=512)
│ ├── config_body.json # Body/object definitions
│ ├── CONFIG.md # Config schema documentation
│ └── legacy_configs/
│ ├── config_cuda.json # Legacy CUDA config (X_1U=128, etc.)
│ ├── config_flowfield.json # Legacy flow field config
│ └── config_gym.json # Legacy gym config
├── models/
│ ├── old/ # Original re100/re200/re400/re50 + vortex
│ ├── 250326/ # Re-trained cloak
│ ├── 250329/ # No-offset cloak
│ ├── 250421/ # Reduced observation
│ ├── 250525/ # Illusion (imit)
│ └── 250729/ # Erase + re-cloak
├── src/
│ ├── drl_pinball/
│ │ ├── knowledge.md ← THIS FILE
│ │ ├── legacy_env/ # Old-API environment classes
│ │ │ ├── legacy_env_karman_cloak_standard.py
│ │ │ ├── legacy_env_erase.py
│ │ │ ├── legacy_env_imit.py
│ │ │ ├── legacy_env_imit_target.py
│ │ │ ├── legacy_env_vortex.py
│ │ │ ├── legacy_env_reduce_obs.py
│ │ │ └── legacy_karman_env.py # Reference implementation (re100)
│ │ ├── legacy_train/ # Training scripts
│ │ │ ├── karman_cloak.py
│ │ │ ├── erase.py
│ │ │ ├── imit.py
│ │ │ ├── vortex.py
│ │ │ └── reduce_obs.py
│ │ └── legacy_test/ # Test/evaluation scripts
│ ├── analysis_crossre/ # Cross-Re analysis (SINDy, etc.)
│ └── CelerisLab/ or LegacyCelerisLab/ # Solver libraries
├── docs/
│ ├── understanding_notes.md # Earlier knowledge consolidation
│ └── My_Confirmation/Chapters/ # LaTeX thesis chapters
├── LegacyCelerisLab/ # Old solver library
└── output/ # Field output, .pkl files
```
---
## 15. Key Numerical Values Quick Reference
| Quantity | Value | Notes |
|----------|-------|-------|
| NX | 1280 | Grid x-dimension |
| NY | 512 | Grid y-dimension |
| L0 | 20 | Base length unit (lattice) |
| U0 | 0.01 | Centerline inlet velocity (lattice) |
| ν (default) | 0.004 | Kinematic viscosity → Re=100 (code) |
| T₀ = D/U₀ | 2000 | Flow-through time (single cylinder diameter) |
| Reynolds (code) | Re = U0·(2D)/ν | Uses 2D reference |
| Reynolds (report) | Re_D = U0·D/ν | Uses 1D reference |
| SAMPLE_INTERVAL | 600-800 | Steps between DRL actions |
| FIFO_LEN | 150 | History buffer length |
| Action scale | 4 or 8 | Scene-dependent |
| Action bias | varies | Scene-dependent |
| Omega guard | [0.01, 1.99] | From config_lbm_pinball.json |
### 15.1 Physics-to-Lattice Unit Relations
```
D (cylinder diameter) = 20 lattice units = 1.0 in L0 units
Single cylinder Re: Re_D = U0 × D / ν = 0.01 × 20 / 0.004 = 50
Code Re: Re_code = U0 × 2D / ν = 0.01 × 40 / 0.004 = 100
```
---
## 16. Important Implementation Notes
### 16.1 Obs Slice Differences
The sensor indices in `obs` are **not** consistent across envs:
- **Cloak/standard**: uses `obs[2:14]` — skips first 2 values (disturbance cylinder forces)
- **Erase**: uses `obs[0:14]` — includes all values (disturbance cylinder forces are part of observation)
- **Illusion/Vortex**: uses `obs[0:12]` — sensors(6) + pinball forces(6)
### 16.2 Center Y Computation
```python
CENTER_Y = (NY - 1) / 2.0 # = 255.5 for NY=512
```
This is because NY is even (512), so center is between two lattice rows.
### 16.3 Force Norm Factors
These are scene-specific constants that must NOT be shared between scenes:
| Scene | force_norm_fact | sens_norm_fact factor |
|-------|----------------|----------------------|
| Cloak/standard | `6 * max(|forces|)` | 5 |
| Erase | `100 * max(|forces|)` | 10 |
| Illusion | `6 * max(|forces|)` | 5 |
| Vortex | `6 * max(|forces|)` | 5 |
| ReducedObs | `10 * max(|forces|)` | 5 |
### 16.4 Vortex Scene Termination
The vortex env is the only scene with **bounded episodes**:
- `MAX_STEPS = 150` (vs 500 for all other scenes)
- `done = self.current_step >= MAX_STEPS`
- This is because the vortex is a transient event that passes through the domain
### 16.5 Experimental Setup (from thesis)
- Water tunnel with towing platform (25cm width)
- Custom force sensor integrating air bearing + 2D force sensor (semiconductor strain gauges)
- Custom low-noise, high-precision data acquisition system
- Planar PIV for flow field measurement
- Current challenges: sensor noise from over-constraint/welding; rail manufacturing precision
---
## 17. Key Differences Between Legacy and New Solvers
| Aspect | Legacy | New |
|--------|--------|-----|
| Solver name | `LegacyCelerisLab` / `CelerisLab` | `CelerisLab` (new) |
| Main class | `FlowField(config_field, config_cuda, device_id)` | `Simulation(config)` |
| Object setup | `add_cylinder()`, `add_sensor()` at runtime | Pre-defined in JSON or added before `initialize()` |
| Checkpoint | `get_ddf()` / `save_ddf()` / `restore_ddf()` / `apply_ddf()` | `snapshot()` / `restore()` or save_checkpoint |
| Force reading | Part of unified `obs` array | `read_force(body_id)` per body |
| Sensor reading | Part of unified `obs` array | `read_sensor(body_id)` per body |
| Torque reading | Not used in legacy (only in ReduceObs) | `read_torque(body_id)` |
| Action smoothing | Built-in (weight=0.1) | None |
| Action application | Single `run(N, action_array)` | `set_body(id, omega=val)` per body then `run(N)` |
| Context management | `context.push()` / `.pop()` for CUDA isolation | Stream-based |
### 17.1 Force/Sensor Value Correction
The fundamental difference in how forces and sensors are accumulated:
**Legacy**: `flow_field.run(N, action)` → internal loop averages over N steps → `obs` contains per-step averages.
**New**: `sim.run(N)` → accelerators accumulate raw sums → `read_force(id)` gives N-step sum → **must divide by N**.
---
*End of comprehensive knowledge document. Last updated: 2026-06-05.*
src/drl_pinball/legacy_env/__init__.py
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src/drl_pinball/legacy_env/legacy_env_erase.py
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# 这个是erase的env，用于训练和评估d1a3o12_250729_250326_erase系列模型
# 上游扰流圆柱，场景与Karman_cloak_standard一致，
# 但是目标是希望pinball后流场跟入口一致，即抹除扰流圆柱尾迹
# 模型名中D代表信号延迟
import gymnasium as gym
import numpy as np
from gymnasium import spaces
import ctypes
from collections import deque
from typing import Tuple
import sys
import os
import matplotlib.pyplot as plt
import queue
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(parent_dir)
from CelerisLab import FlowField
from CelerisLab import utils
config_cuda = utils.load_cuda_config(
os.path.join(parent_dir, "configs", "config_cuda.json")
)
config_field = utils.load_flow_field_config(
os.path.join(parent_dir, "configs", "config_flowfield.json")
)
S_DIM, A_DIM = 12, 3
U0 = config_field.velocity
T0 = 1000
SAMPLE_INTERVAL = 600
FIFO_LEN = 150
CONV_LEN = 36
MAX_STEPS = 500
if config_field.data_type == "FP32":
DATA_TYPE = np.float32
else:
raise ValueError(f"Unsupported data type {config_field.data_type}.")
class CustomEnv(gym.Env):
"""Custom Environment that follows gym interface."""
metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL}
def __init__(self, device_id=0):
super().__init__()
self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
self.observation_space = spaces.Box(
low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE
)
self.fifo_states = deque(maxlen=FIFO_LEN)
self.target_states = np.empty((0, 6), dtype=DATA_TYPE)
self.force_norm_fact = 1.0
self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
self.reward_u = 0.0
self.reward_v = 0.0
self.reward_sim = 0.0
self.current_step = 0
self.flow_field = FlowField(config_field, config_cuda, device_id)
L0 = 20
U0 = config_field.velocity
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
# center: Tuple[float, float, float] = (10 * L0, (NY - 1) / 2, 0)
# self.flow_field.add_cylinder(center, L0)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
self.flow_field.run(int(2*NX/U0), np.zeros(3, dtype=DATA_TYPE))
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(3, dtype=DATA_TYPE))
new_state = self.flow_field.obs.copy()[0:6]
self.target_states = np.vstack((self.target_states, new_state))
self.target_sensors = np.mean(self.target_states, axis=0)
# self.flow_field.apply_ddf()
center: Tuple[float, float, float] = (10 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, 0.75*L0)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
self.flow_field.run(int(4*NX/U0), np.zeros(7, dtype=DATA_TYPE))
self.flow_field.get_ddf()
self.flow_field.save_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(7, dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[0:14])
temp_states = np.array(self.fifo_states)
self.force_norm_fact = 100 * np.max(np.abs(temp_states[:, 8:14]))
for i in range(6):
self.sens_deviation[i] = np.mean(temp_states[:, i])
self.sens_norm_fact[i] = 10 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i]))
self.flow_field.apply_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.array([0.0, 0.0, 0.0, 0.0, 0.0, -8*U0, 8*U0], dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[0:14])
self.save_states = self.fifo_states.copy()
self.flow_field.apply_ddf()
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (
action,
type(action),
)
# barrier = threading.Barrier(2)
result_queue = queue.Queue()
def run_flow_field(action):
self.flow_field.context.push()
U0 = config_field.velocity
try:
temp = np.zeros(7, dtype=DATA_TYPE)
temp[4:7] = np.array((action*8+[0,-8,8])*U0, dtype=DATA_TYPE)
self.flow_field.run(SAMPLE_INTERVAL, temp)
finally:
self.flow_field.context.pop()
# barrier.wait()
self.fifo_states.append(self.flow_field.obs.copy()[0:14])
def proc_data():
states = np.array(self.fifo_states)
forces = states[-1, 6:14] / self.force_norm_fact
cd = (forces[2] + forces[4] + forces[6]) / 3
cl = (forces[3] + forces[5] + forces[7]) / 3
sens = (states[-1, 0:6] - self.sens_deviation) / self.sens_norm_fact
target_sens = (self.target_sensors - self.sens_deviation) / self.sens_norm_fact
similarities = 0.0
def calc_lag(target, state):
target_mean = np.mean(target)
state_mean = np.mean(state)
correlation = np.correlate(target - target_mean, state - state_mean, "full")
lags = np.arange(-len(target) + 1, len(target))
max_lag = lags[np.argmax(correlation)]
return max_lag
def calc_sim(target, state):
# 计算幅值差异权重
target_std = np.std(target) if np.std(target) > 1e-8 else 1e-8
state_std = np.std(state) if np.std(state) > 1e-8 else 1e-8
amplitude_ratio = min(target_std, state_std) / max(target_std, state_std)
# 计算均值差异
mean_diff = abs(np.mean(target) - np.mean(state))
max_scale = max(abs(np.mean(target)), abs(np.mean(state)), 1e-8)
mean_similarity = 1 / (1 + mean_diff / max_scale * 10)
# DTW计算
n = len(target)
m = len(state)
dtw_matrix = np.full((n + 1, m + 1), np.inf)
dtw_matrix[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(target[i - 1] - state[j - 1])
last_min = min(dtw_matrix[i - 1, j],
dtw_matrix[i, j - 1],
dtw_matrix[i - 1, j - 1])
dtw_matrix[i, j] = cost + last_min
# 改进的归一化方法
max_possible_cost = max(np.max(np.abs(target)), np.max(np.abs(state)), 1e-8)
dtw_distance = dtw_matrix[n, m] / (len(target) * max_possible_cost)
DTW_similarity = max(0, 1 - dtw_distance)
# 综合相似度：形状相似度 * 幅值相似度 * 均值相似度
total_similarity = 0.8 * DTW_similarity + 0.1 * amplitude_ratio + 0.1 * mean_similarity
return total_similarity
# id_sens = 1
# target_seq = self.target_states[CONV_LEN:2*CONV_LEN, id_sens]
target_seq = -states[CONV_LEN:2*CONV_LEN, 7]
# state_seq = states[-CONV_LEN:, id_sens]
state_seq = states[-CONV_LEN:, 9]
lag = calc_lag(target_seq, state_seq)
for i in range(0, 2):
target_seq = -np.roll(states[:, i+6], -lag)[CONV_LEN:2*CONV_LEN]
state_seq = states[-CONV_LEN:, i+8] + states[-CONV_LEN:, i+10] + states[-CONV_LEN:, i+12]
similarities += calc_sim(target_seq, state_seq) / 2
diff_u = (np.abs(sens[0] - target_sens[0]) + np.abs(sens[2] - target_sens[2]) + np.abs(sens[4] - target_sens[4]))/3
diff_v = (np.abs(sens[1] - target_sens[1]) + np.abs(sens[3] - target_sens[3]) + np.abs(sens[5] - target_sens[5]))/3
mean_u = np.mean(np.abs(states[:, 0] - self.target_sensors[0]) \
+ np.abs(states[:, 2] - self.target_sensors[2]) \
+ np.abs(states[:, 4] - self.target_sensors[4])) / self.sens_norm_fact[2]
amp_v = np.std(states[:, 1] + states[:, 3] + states[:, 5]) / self.sens_norm_fact[3]
self.reward_u = np.exp(-np.abs(diff_u * 40))
self.reward_v = 0.7 * np.exp(-np.abs(amp_v * 20)) + 0.3 * np.exp(-np.abs(diff_v * 20))
# self.reward_sim = np.exp(-50*np.abs(similarities - 1)**2)
self.reward_sim = similarities
reward = np.minimum(0.4 * self.reward_u + 0.4 * self.reward_v + 0.2 * self.reward_sim, 1.0)
result_queue.put((np.hstack([forces[2:8], sens]), reward))
run_flow_field(action)
proc_data()
observation, reward = result_queue.get()
truncated = bool(np.any(observation > 1) or np.any(observation < -1))
observation = np.clip(observation, -1, 1)
self.current_step += 1
# done = self.current_step >= MAX_STEPS
done = False
return observation, float(reward), done, truncated, {}
def reset(self, seed=None):
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
self.fifo_states = self.save_states.copy()
self.current_step = 0
return np.zeros(S_DIM, dtype=np.float32), {}
def render(self, mode="human"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
speed = np.sqrt(ux**2 + uy**2)
plt.figure(figsize=(10, 5))
plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY])
plt.colorbar(label='Speed')
plt.title('Scalar Velocity Field')
plt.xlabel('X')
plt.ylabel('Y')
plt.tight_layout()
plt.show()
def save_field(self, filename):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
def average_field(self, mode=["add", "save", "clear"], filename="average_field.dat"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_new = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
if "add" in mode:
self.ddf_ave = self.ddf_ave + ddf_new
self.ddf_ave_cont += 1
if "save" in mode:
if self.ddf_ave_cont == 0:
raise ValueError("No data to save. Please run 'add' mode first.")
ux = (self.ddf_ave[:, :, 1] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 8] - self.ddf_ave[:, :, 3] - self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 7]) / U0 / self.ddf_ave_cont
uy = (self.ddf_ave[:, :, 2] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 4] - self.ddf_ave[:, :, 7] - self.ddf_ave[:, :, 8]) / U0 / self.ddf_ave_cont
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
print(f"Average field amount: {self.ddf_ave_cont}")
if "clear" in mode:
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
def close(self):
self.flow_field.__del__()
src/drl_pinball/legacy_env/legacy_env_imit.py
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# 这个是imit圆柱的env，用于训练和评估d1a3o14_250525_imit系列模型
# 上游干净来流，目标是pinball后流场跟设定尺寸圆柱一致
# 模型名中L代表目标直径，S代表SAMPLE_INTERVAL
import gymnasium as gym
import numpy as np
from gymnasium import spaces
import ctypes
from collections import deque
from typing import Tuple
import sys
import os
import matplotlib.pyplot as plt
import queue
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(parent_dir)
from CelerisLab import FlowField
from CelerisLab import utils
config_cuda = utils.load_cuda_config(
os.path.join(parent_dir, "configs", "config_cuda.json")
)
config_field = utils.load_flow_field_config(
os.path.join(parent_dir, "configs", "config_flowfield.json")
)
S_DIM, A_DIM = 14, 3
U0 = config_field.velocity
T0 = 1000
SAMPLE_INTERVAL = 600 # 这里会随着圆柱尺寸和粘性变动，在模型名中体现
FIFO_LEN = 150
CONV_LEN = 36
MAX_STEPS = 500
if config_field.data_type == "FP32":
DATA_TYPE = np.float32
else:
raise ValueError(f"Unsupported data type {config_field.data_type}.")
class CustomEnv(gym.Env):
"""Custom Environment that follows gym interface."""
metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL}
def __init__(self, device_id=0):
super().__init__()
self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
self.observation_space = spaces.Box(
low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE
)
self.fifo_states = deque(maxlen=FIFO_LEN)
self.target_states = np.empty((0, 8), dtype=DATA_TYPE)
self.force_norm_fact = 1.0
self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
self.reward_cd = 0.0
self.reward_cl = 0.0
self.reward_sim = 0.0
self.current_step = 0
self.flow_field = FlowField(config_field, config_cuda, device_id)
L0 = 20
U0 = config_field.velocity
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
center: Tuple[float, float, float] = (20 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, 1.5*L0) # 这里会随着圆柱尺寸变动，在模型名中体现
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
self.flow_field.run(int(4*NX/U0), np.zeros(4, dtype=DATA_TYPE))
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE))
new_state = self.flow_field.obs.copy()[0:8]
self.target_states = np.vstack((self.target_states, new_state))
def analyze_harmonics(states, n_harmonics):
N, D = states.shape
result = []
for d in range(D):
y = states[:, d]
fft_coef = np.fft.rfft(y)
freqs = np.fft.rfftfreq(N, d=1)
amps = 2 * np.abs(fft_coef) / N
phases = np.angle(fft_coef)
idx = np.argsort(amps[1:])[::-1][:n_harmonics] + 1
harmonics = {
'dc': np.real(fft_coef[0]) / N,
'amps': amps[idx],
'freqs': freqs[idx],
'phases': phases[idx]
}
result.append(harmonics)
return result
self.target_harmonics = analyze_harmonics(self.target_states, n_harmonics=5)
del self.flow_field
self.flow_field = FlowField(config_field, config_cuda, device_id)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (19 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (20.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (20.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
self.flow_field.run(int(4*NX/U0), np.zeros(6, dtype=DATA_TYPE))
self.flow_field.get_ddf()
self.flow_field.save_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(6, dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[0:12])
temp_states = np.array(self.fifo_states)
self.force_norm_fact = 6 * np.max(np.abs(temp_states[:, 6:12]))
for i in range(6):
self.sens_deviation[i] = np.mean(temp_states[:, i])
self.sens_norm_fact[i] = 5 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i]))
self.flow_field.apply_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.array([0.0, 0.0, 0.0, 0.0, -1*U0, 1*U0], dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[0:12])
self.save_states = self.fifo_states.copy()
self.flow_field.get_ddf()
self.flow_field.save_ddf()
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (
action,
type(action),
)
# barrier = threading.Barrier(2)
result_queue = queue.Queue()
def run_flow_field(action):
self.flow_field.context.push()
U0 = config_field.velocity
try:
temp = np.zeros(6, dtype=DATA_TYPE)
temp[3:6] = np.array((action*8+[0,-2,2])*U0, dtype=DATA_TYPE)
self.flow_field.run(SAMPLE_INTERVAL, temp)
finally:
self.flow_field.context.pop()
# barrier.wait()
self.fifo_states.append(self.flow_field.obs.copy()[0:12])
def proc_data():
states = np.array(self.fifo_states)
forces = states[-1, 6:12] / self.force_norm_fact
cd = forces[0] + forces[2] + forces[4]
cl = forces[1] + forces[3] + forces[5]
sens = (states[-1, 0:6] - self.sens_deviation) / self.sens_norm_fact
similarities = 0.0
def calc_lag(target, state):
target_mean = np.mean(target)
state_mean = np.mean(state)
correlation = np.correlate(target - target_mean, state - state_mean, "full")
lags = np.arange(-len(target) + 1, len(target))
max_lag = lags[np.argmax(correlation)]
return max_lag
def calc_sim(target, state):
n = len(target)
m = len(state)
dtw_matrix = np.full((n + 1, m + 1), np.inf)
dtw_matrix[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(target[i - 1] - state[j - 1])
last_min = min(dtw_matrix[i - 1, j],
dtw_matrix[i, j - 1],
dtw_matrix[i - 1, j - 1])
dtw_matrix[i, j] = cost + last_min
return 1 - (dtw_matrix[n, m] / len(target))
def gen_target_states_at(t, harmonics):
t = np.asarray(t)
D = len(harmonics)
result = np.zeros((t.size, D), dtype=np.float32)
for d, h in enumerate(harmonics):
val = np.full(t.shape, h['dc'], dtype=np.float32)
for amp, freq, phase in zip(h['amps'], h['freqs'], h['phases']):
val += amp * np.cos(2 * np.pi * freq * t + phase)
result[:, d] = val
if result.shape[0] == 1:
return result[0]
return result
id_sens = 1
target_seq = self.target_states[CONV_LEN:2*CONV_LEN, id_sens+2]
state_seq = states[-CONV_LEN:, id_sens]
lag = calc_lag(target_seq, state_seq)
for i in range(0, 6):
target_seq = np.roll(self.target_states[:, i+2], -lag)[CONV_LEN:2*CONV_LEN]
state_seq = states[-CONV_LEN:, i]
similarities += calc_sim(target_seq, state_seq) / 6
target_states = gen_target_states_at(self.current_step, self.target_harmonics)
target_cd = target_states[0] / self.force_norm_fact
target_cl = target_states[1] / self.force_norm_fact
self.reward_cd = np.exp(-np.abs((cd-target_cd) * 10))
self.reward_cl = np.exp(-np.abs((cl-target_cl) * 10))
self.reward_sim = np.exp(-10*np.abs(similarities - 1))
reward = np.minimum(0.3 * self.reward_cd + 0.3 * self.reward_cl + 0.4 * self.reward_sim, 1.0)
result_queue.put((np.hstack([forces, sens, target_cd, target_cl]), reward))
run_flow_field(action)
proc_data()
observation, reward = result_queue.get()
truncated = bool(np.any(observation > 1) or np.any(observation < -1))
observation = np.clip(observation, -1, 1)
self.current_step += 1
# done = self.current_step >= MAX_STEPS
done = False
return observation, float(reward), done, truncated, {}
def reset(self, seed=None):
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
self.fifo_states = self.save_states.copy()
self.current_step = 0
return np.zeros(S_DIM, dtype=np.float32), {}
def render(self, mode="human"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
speed = np.sqrt(ux**2 + uy**2)
plt.figure(figsize=(10, 5))
plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY])
plt.colorbar(label='Speed')
plt.title('Scalar Velocity Field')
plt.xlabel('X')
plt.ylabel('Y')
plt.tight_layout()
plt.show()
def save_field(self, filename):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
def average_field(self, mode=["add", "save", "clear"], filename="average_field.dat"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_new = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
if "add" in mode:
self.ddf_ave = self.ddf_ave + ddf_new
self.ddf_ave_cont += 1
if "save" in mode:
if self.ddf_ave_cont == 0:
raise ValueError("No data to save. Please run 'add' mode first.")
ux = (self.ddf_ave[:, :, 1] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 8] - self.ddf_ave[:, :, 3] - self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 7]) / U0 / self.ddf_ave_cont
uy = (self.ddf_ave[:, :, 2] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 4] - self.ddf_ave[:, :, 7] - self.ddf_ave[:, :, 8]) / U0 / self.ddf_ave_cont
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
print(f"Average field amount: {self.ddf_ave_cont}")
if "clear" in mode:
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
def close(self):
self.flow_field.__del__()
src/drl_pinball/legacy_env/legacy_env_imit_target.py
+207
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@@ -0,0 +1,207 @@
# 这个是imit圆柱的env，用于产生d1a3o14_250525_imit系列模型的目标流场
# 跟随模型，要匹配圆柱直径和采样频率
# 模型名中L代表目标直径，S代表SAMPLE_INTERVAL
import gymnasium as gym
import numpy as np
from gymnasium import spaces
import ctypes
from collections import deque
from typing import Tuple
import sys
import os
import matplotlib.pyplot as plt
import queue
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(parent_dir)
from CelerisLab import FlowField
from CelerisLab import utils
config_cuda = utils.load_cuda_config(
os.path.join(parent_dir, "configs", "config_cuda.json")
)
config_field = utils.load_flow_field_config(
os.path.join(parent_dir, "configs", "config_flowfield.json")
)
S_DIM, A_DIM = 14, 3
U0 = config_field.velocity
T0 = 1000
SAMPLE_INTERVAL = 600 # 这里会随着圆柱尺寸和粘性变动，在模型名中体现
FIFO_LEN = 150
CONV_LEN = 36
MAX_STEPS = 500
if config_field.data_type == "FP32":
DATA_TYPE = np.float32
else:
raise ValueError(f"Unsupported data type {config_field.data_type}.")
class CustomEnv(gym.Env):
"""Custom Environment that follows gym interface."""
metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL}
def __init__(self, device_id=0):
super().__init__()
self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
self.observation_space = spaces.Box(
low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE
)
self.fifo_states = deque(maxlen=FIFO_LEN)
self.target_states = np.empty((0, 8), dtype=DATA_TYPE)
self.force_norm_fact = 1.0
self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
self.reward_cd = 0.0
self.reward_cl = 0.0
self.reward_sim = 0.0
self.current_step = 0
self.flow_field = FlowField(config_field, config_cuda, device_id)
L0 = 20
U0 = config_field.velocity
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
center: Tuple[float, float, float] = (20 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, 1*L0) # 这里会随着圆柱尺寸变动，在模型名中体现
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
self.flow_field.run(int(4*NX/U0), np.zeros(4, dtype=DATA_TYPE))
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE))
new_state = self.flow_field.obs.copy()[0:8]
self.target_states = np.vstack((self.target_states, new_state))
def analyze_harmonics(states, n_harmonics):
N, D = states.shape
result = []
for d in range(D):
y = states[:, d]
fft_coef = np.fft.rfft(y)
freqs = np.fft.rfftfreq(N, d=1)
amps = 2 * np.abs(fft_coef) / N
phases = np.angle(fft_coef)
idx = np.argsort(amps[1:])[::-1][:n_harmonics] + 1
harmonics = {
'dc': np.real(fft_coef[0]) / N,
'amps': amps[idx],
'freqs': freqs[idx],
'phases': phases[idx]
}
result.append(harmonics)
return result
self.target_harmonics = analyze_harmonics(self.target_states, n_harmonics=5)
self.flow_field.get_ddf()
self.flow_field.save_ddf()
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (
action,
type(action),
)
# barrier = threading.Barrier(2)
result_queue = queue.Queue()
def run_flow_field(action):
self.flow_field.context.push()
U0 = config_field.velocity
try:
temp = np.zeros(4, dtype=DATA_TYPE)
self.flow_field.run(SAMPLE_INTERVAL, temp)
finally:
self.flow_field.context.pop()
# barrier.wait()
run_flow_field(action)
truncated = False
observation = np.zeros(14, dtype=DATA_TYPE)
self.current_step += 1
# done = self.current_step >= MAX_STEPS
done = False
return observation, float(0), done, truncated, {}
def reset(self, seed=None):
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
self.current_step = 0
return np.zeros(S_DIM, dtype=np.float32), {}
def render(self, mode="human"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
speed = np.sqrt(ux**2 + uy**2)
plt.figure(figsize=(10, 5))
plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY])
plt.colorbar(label='Speed')
plt.title('Scalar Velocity Field')
plt.xlabel('X')
plt.ylabel('Y')
plt.tight_layout()
plt.show()
def save_field(self, filename):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
def average_field(self, mode=["add", "save", "clear"], filename="average_field.dat"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_new = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
if "add" in mode:
self.ddf_ave = self.ddf_ave + ddf_new
self.ddf_ave_cont += 1
if "save" in mode:
if self.ddf_ave_cont == 0:
raise ValueError("No data to save. Please run 'add' mode first.")
ux = (self.ddf_ave[:, :, 1] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 8] - self.ddf_ave[:, :, 3] - self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 7]) / U0 / self.ddf_ave_cont
uy = (self.ddf_ave[:, :, 2] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 4] - self.ddf_ave[:, :, 7] - self.ddf_ave[:, :, 8]) / U0 / self.ddf_ave_cont
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
print(f"Average field amount: {self.ddf_ave_cont}")
if "clear" in mode:
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
def close(self):
self.flow_field.__del__()
src/drl_pinball/legacy_env/legacy_env_karman_cloak_standard.py
+270
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# 这个是Karman_cloak_standard的env，用于训练和评估d1a3o12_re系列模型和250326模型
# 上游一个2D扰流圆柱，目标是pinball后流场跟无pinball一致
import gymnasium as gym
import numpy as np
from gymnasium import spaces
import ctypes
from collections import deque
from typing import Tuple
import sys
import os
import matplotlib.pyplot as plt
import queue
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(parent_dir)
from CelerisLab import FlowField
from CelerisLab import utils
config_cuda = utils.load_cuda_config(
os.path.join(parent_dir, "configs", "config_cuda.json")
)
config_field = utils.load_flow_field_config(
os.path.join(parent_dir, "configs", "config_flowfield.json")
)
S_DIM, A_DIM = 12, 3
U0 = config_field.velocity
T0 = 1000
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
CONV_LEN = 30
MAX_STEPS = 500
if config_field.data_type == "FP32":
DATA_TYPE = np.float32
else:
raise ValueError(f"Unsupported data type {config_field.data_type}.")
class CustomEnv(gym.Env):
"""Custom Environment that follows gym interface."""
metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL}
def __init__(self, device_id=0):
super().__init__()
self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
self.observation_space = spaces.Box(
low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE
)
self.fifo_states = deque(maxlen=FIFO_LEN)
self.target_states = np.empty((0, 6), dtype=DATA_TYPE)
self.force_norm_fact = 1.0
self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
self.reward_cd = 0.0
self.reward_cl = 0.0
self.reward_sim = 0.0
self.current_step = 0
self.flow_field = FlowField(config_field, config_cuda, device_id)
L0 = 20
U0 = config_field.velocity
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
center: Tuple[float, float, float] = (10 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
self.flow_field.run(int(4*NX/U0), np.zeros(4, dtype=DATA_TYPE))
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE))
new_state = self.flow_field.obs.copy()[2:8]
self.target_states = np.vstack((self.target_states, new_state))
# self.flow_field.apply_ddf()
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
self.flow_field.run(int(4*NX/U0), np.zeros(7, dtype=DATA_TYPE))
self.flow_field.get_ddf()
self.flow_field.save_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(7, dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
temp_states = np.array(self.fifo_states)
self.force_norm_fact = 6 * np.max(np.abs(temp_states[:, 6:12]))
for i in range(6):
self.sens_deviation[i] = np.mean(temp_states[:, i])
self.sens_norm_fact[i] = 5 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i]))
self.flow_field.apply_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.array([0.0, 0.0, 0.0, 0.0, 0.0, -4*U0, 4*U0], dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
self.save_states = self.fifo_states.copy()
self.flow_field.apply_ddf()
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (
action,
type(action),
)
# barrier = threading.Barrier(2)
result_queue = queue.Queue()
def run_flow_field(action):
self.flow_field.context.push()
U0 = config_field.velocity
try:
temp = np.zeros(7, dtype=DATA_TYPE)
temp[4:7] = np.array((action*8+[0,-4,4])*U0, dtype=DATA_TYPE)
self.flow_field.run(SAMPLE_INTERVAL, temp)
finally:
self.flow_field.context.pop()
# barrier.wait()
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
def proc_data():
states = np.array(self.fifo_states)
forces = states[-1, 6:12] / self.force_norm_fact
cd = (forces[0] + forces[2] + forces[4]) / 3
cl = (forces[1] + forces[3] + forces[5]) / 3
sens = (states[-1, 0:6] - self.sens_deviation) / self.sens_norm_fact
similarities = 0.0
def calc_lag(target, state):
target_mean = np.mean(target)
state_mean = np.mean(state)
correlation = np.correlate(target - target_mean, state - state_mean, "full")
lags = np.arange(-len(target) + 1, len(target))
max_lag = lags[np.argmax(correlation)]
return max_lag
def calc_sim(target, state):
n = len(target)
m = len(state)
dtw_matrix = np.full((n + 1, m + 1), np.inf)
dtw_matrix[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(target[i - 1] - state[j - 1])
last_min = min(dtw_matrix[i - 1, j],
dtw_matrix[i, j - 1],
dtw_matrix[i - 1, j - 1])
dtw_matrix[i, j] = cost + last_min
return 1 - (dtw_matrix[n, m] / len(target))
id_sens = 1
target_seq = self.target_states[CONV_LEN:2*CONV_LEN, id_sens]
state_seq = states[-CONV_LEN:, id_sens]
lag = calc_lag(target_seq, state_seq)
for i in range(0, 6):
target_seq = np.roll(self.target_states[:, i], -lag)[CONV_LEN:2*CONV_LEN]
state_seq = states[-CONV_LEN:, i]
similarities += calc_sim(target_seq, state_seq) / 6
self.reward_cd = np.exp(-np.abs(cd * 20))
self.reward_cl = np.exp(-np.abs(cl * 80))
self.reward_sim = np.exp(-10*np.abs(similarities - 1))
reward = np.minimum(0.3 * self.reward_cd + 0.4 * self.reward_cl + 0.3 * self.reward_sim, 1.0)
result_queue.put((np.hstack([forces, sens]), reward))
run_flow_field(action)
proc_data()
observation, reward = result_queue.get()
truncated = bool(np.any(observation > 1) or np.any(observation < -1))
observation = np.clip(observation, -1, 1)
self.current_step += 1
# done = self.current_step >= MAX_STEPS
done = False
return observation, float(reward), done, truncated, {}
def reset(self, seed=None):
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
self.fifo_states = self.save_states.copy()
self.current_step = 0
return np.zeros(S_DIM, dtype=np.float32), {}
def render(self, mode="human"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
speed = np.sqrt(ux**2 + uy**2)
plt.figure(figsize=(10, 5))
plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY])
plt.colorbar(label='Speed')
plt.title('Scalar Velocity Field')
plt.xlabel('X')
plt.ylabel('Y')
plt.tight_layout()
plt.show()
def save_field(self, filename):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
def average_field(self, mode=["add", "save", "clear"], filename="average_field.dat"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_new = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
if "add" in mode:
self.ddf_ave = self.ddf_ave + ddf_new
self.ddf_ave_cont += 1
if "save" in mode:
if self.ddf_ave_cont == 0:
raise ValueError("No data to save. Please run 'add' mode first.")
ux = (self.ddf_ave[:, :, 1] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 8] - self.ddf_ave[:, :, 3] - self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 7]) / U0 / self.ddf_ave_cont
uy = (self.ddf_ave[:, :, 2] + self.ddf_ave[:, :, 5] + self.ddf_ave[:, :, 6] - self.ddf_ave[:, :, 4] - self.ddf_ave[:, :, 7] - self.ddf_ave[:, :, 8]) / U0 / self.ddf_ave_cont
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
print(f"Average field amount: {self.ddf_ave_cont}")
if "clear" in mode:
self.ddf_ave = np.zeros((NX, NY, 9), dtype=DATA_TYPE)
self.ddf_ave_cont = 0
def close(self):
self.flow_field.__del__()
src/drl_pinball/legacy_env/legacy_env_reduce_obs.py
+245
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@@ -0,0 +1,245 @@
# 这个是reduce_obs的env，用于训练和评估d1a3o12_250421系列模型
# 上游扰流圆柱，场景与Karman_cloak_standard一致
# obs从12逐渐减少至2，观察模型是否能够适应，具体观察量在模型名中体现
import gymnasium as gym
import numpy as np
from gymnasium import spaces
import ctypes
from collections import deque
from typing import Tuple
import sys
import os
import matplotlib.pyplot as plt
import queue
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(parent_dir)
from CelerisLab import FlowField
from CelerisLab import utils
config_cuda = utils.load_cuda_config(
os.path.join(parent_dir, "configs", "config_cuda.json")
)
config_field = utils.load_flow_field_config(
os.path.join(parent_dir, "configs", "config_flowfield.json")
)
S_DIM, A_DIM = 3, 3 # 这里会随着obs数量变动，在模型名中体现
U0 = config_field.velocity
T0 = 1000
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
CONV_LEN = 36
MAX_STEPS = 500
if config_field.data_type == "FP32":
DATA_TYPE = np.float32
else:
raise ValueError(f"Unsupported data type {config_field.data_type}.")
class CustomEnv(gym.Env):
"""Custom Environment that follows gym interface."""
metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL}
def __init__(self, device_id=0):
super().__init__()
self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
self.observation_space = spaces.Box(
low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE
)
self.fifo_states = deque(maxlen=FIFO_LEN)
self.target_states = np.empty((0, 6), dtype=DATA_TYPE)
self.force_norm_fact = 1.0
self.torque_norm_fact = 1.0
self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
self.reward_cd = 0.0
self.reward_cl = 0.0
self.reward_sim = 0.0
self.current_step = 0
self.flow_field = FlowField(config_field, config_cuda, device_id)
L0 = 20
U0 = config_field.velocity
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
center: Tuple[float, float, float] = (10 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
self.flow_field.run(int(4*NX/U0), np.zeros(4, dtype=DATA_TYPE))
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE))
new_state = self.flow_field.obs.copy()[2:8]
self.target_states = np.vstack((self.target_states, new_state))
# self.flow_field.apply_ddf()
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
self.flow_field.run(int(4*NX/U0), np.zeros(7, dtype=DATA_TYPE))
self.flow_field.get_ddf()
self.flow_field.save_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(7, dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
temp_states = np.array(self.fifo_states)
self.force_norm_fact = 10 * np.max(np.abs(temp_states[:, 6:12]))
temp_torque = -temp_states[:, 1] - temp_states[:, 2]*np.sqrt(3)/2 + temp_states[:, 3]/2 + temp_states[:, 4]*np.sqrt(3)/2 + temp_states[:, 5]/2
self.torque_norm_fact = 10 * np.max(np.abs(temp_torque))
for i in range(6):
self.sens_deviation[i] = np.mean(temp_states[:, i])
self.sens_norm_fact[i] = 5 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i]))
self.flow_field.apply_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.array([0.0, 0.0, 0.0, 0.0, 0.0, -4*U0, 4*U0], dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
self.save_states = self.fifo_states.copy()
self.flow_field.apply_ddf()
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (
action,
type(action),
)
# barrier = threading.Barrier(2)
result_queue = queue.Queue()
def run_flow_field(action):
self.flow_field.context.push()
U0 = config_field.velocity
try:
temp = np.zeros(7, dtype=DATA_TYPE)
temp[4:7] = np.array((action*8+[0,-4,4])*U0, dtype=DATA_TYPE)
self.flow_field.run(SAMPLE_INTERVAL, temp)
finally:
self.flow_field.context.pop()
# barrier.wait()
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
def proc_data():
states = np.array(self.fifo_states)
forces = states[-1, 6:12] / self.force_norm_fact
obs_torque = (-states[-1, 1] - states[-1, 2]*np.sqrt(3)/2 + states[-1, 3]/2 + states[-1, 4]*np.sqrt(3)/2 + states[-1, 5]/2) / self.torque_norm_fact
obs_drag = (forces[0] + forces[2] + forces[4]) / 3
obs_lift = (forces[1] + forces[3] + forces[5]) / 3
sens = (states[-1, 0:6] - self.sens_deviation) / self.sens_norm_fact
similarities = 0.0
def calc_lag(target, state):
target_mean = np.mean(target)
state_mean = np.mean(state)
correlation = np.correlate(target - target_mean, state - state_mean, "full")
lags = np.arange(-len(target) + 1, len(target))
max_lag = lags[np.argmax(correlation)]
return max_lag
def calc_sim(target, state):
n = len(target)
m = len(state)
dtw_matrix = np.full((n + 1, m + 1), np.inf)
dtw_matrix[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(target[i - 1] - state[j - 1])
last_min = min(dtw_matrix[i - 1, j],
dtw_matrix[i, j - 1],
dtw_matrix[i - 1, j - 1])
dtw_matrix[i, j] = cost + last_min
return 1 - (dtw_matrix[n, m] / len(target))
id_sens = 1
target_seq = self.target_states[CONV_LEN:2*CONV_LEN, id_sens]
state_seq = states[-CONV_LEN:, id_sens]
lag = calc_lag(target_seq, state_seq)
for i in range(0, 6):
target_seq = np.roll(self.target_states[:, i], -lag)[CONV_LEN:2*CONV_LEN]
state_seq = states[-CONV_LEN:, i]
similarities += calc_sim(target_seq, state_seq) / 6
self.reward_cd = np.exp(-np.abs(obs_drag * 20))
self.reward_cl = np.exp(-np.abs(obs_lift * 80))
self.reward_sim = np.exp(-10*np.abs(similarities - 1))
reward = np.minimum(0.3 * self.reward_cd + 0.3 * self.reward_cl + 0.4 * self.reward_sim, 1.0)
result_queue.put((np.hstack([obs_torque, forces[0:2]]), reward)) # 这里会随着obs数量变动，在模型名中体现
run_flow_field(action)
proc_data()
observation, reward = result_queue.get()
truncated = bool(np.any(observation > 1) or np.any(observation < -1))
observation = np.clip(observation, -1, 1)
self.current_step += 1
# done = self.current_step >= MAX_STEPS
done = False
return observation, float(reward), done, truncated, {}
def reset(self, seed=None):
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
self.fifo_states = self.save_states.copy()
self.current_step = 0
return np.zeros(S_DIM, dtype=np.float32), {}
def render(self, mode="human"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
speed = np.sqrt(ux**2 + uy**2)
plt.figure(figsize=(10, 5))
plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY])
plt.colorbar(label='Speed')
plt.title('Scalar Velocity Field')
plt.xlabel('X')
plt.ylabel('Y')
plt.tight_layout()
plt.show()
def save_field(self, filename):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
def close(self):
self.flow_field.__del__()
src/drl_pinball/legacy_env/legacy_env_vortex.py
+237
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@@ -0,0 +1,237 @@
# 这个是vortex的env，用于训练和评估vortex_taylor和vortex_lamb模型
# 上游干净来流，目标是vortex流过的时序信号于无pinball情况一致
# vortes系列模型都基于d1a3o12_re100模型迁移训练
import gymnasium as gym
import numpy as np
from gymnasium import spaces
import ctypes
from collections import deque
from typing import Tuple
import sys
import os
import matplotlib.pyplot as plt
import queue
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(parent_dir)
from CelerisLab import FlowField
from CelerisLab import utils
config_cuda = utils.load_cuda_config(
os.path.join(parent_dir, "configs", "config_cuda.json")
)
config_field = utils.load_flow_field_config(
os.path.join(parent_dir, "configs", "config_flowfield.json")
)
S_DIM, A_DIM = 12, 3
U0 = config_field.velocity
T0 = 1000
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
CONV_LEN = 30
MAX_STEPS = 150
if config_field.data_type == "FP32":
DATA_TYPE = np.float32
else:
raise ValueError(f"Unsupported data type {config_field.data_type}.")
class CustomEnv(gym.Env):
"""Custom Environment that follows gym interface."""
metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL}
def __init__(self, device_id=0):
super().__init__()
self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
self.observation_space = spaces.Box(
low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE
)
self.fifo_states = deque(maxlen=FIFO_LEN)
self.target_states = np.empty((0, 6), dtype=DATA_TYPE)
self.force_norm_fact = 1.0
self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
self.reward_cd = 0.0
self.reward_cl = 0.0
self.reward_sim = 0.0
self.current_step = 0
self.flow_field = FlowField(config_field, config_cuda, device_id)
L0 = 20
U0 = config_field.velocity
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
self.flow_field.run(int(1*NX/U0), np.zeros(3, dtype=DATA_TYPE))
self.flow_field.get_ddf()
self.flow_field.save_ddf()
center: Tuple[float, float, float] = (10 * L0, (NY - 1) / 2, 0)
# self.flow_field.add_vortex(center, L0 * 2, 0.5*U0, 0, "lamb")
self.flow_field.add_vortex(center, L0 * 2, 0.03*U0, 0, "taylor") # 这里会更改vortex类型，在模型名中体现
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(3, dtype=DATA_TYPE))
new_state = self.flow_field.obs.copy()
self.target_states = np.vstack((self.target_states, new_state))
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
self.flow_field.run(int(1*NX/U0), np.zeros(6, dtype=DATA_TYPE))
self.flow_field.run(int(1*NX/U0), np.array([0.0, 0.0, 0.0, 0.0, -4*U0, 4*U0], dtype=DATA_TYPE))
# self.flow_field.get_ddf()
# self.flow_field.save_ddf()
center: Tuple[float, float, float] = (15 * L0, (NY - 1) / 2, 0)
# self.flow_field.add_vortex(center, L0 * 2, 0.5*U0, 0, "lamb")
self.flow_field.add_vortex(center, L0 * 2, 0.03*U0, 0, "taylor")
self.flow_field.save_ddf()
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(6, dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy())
# self.save_states = self.fifo_states.copy()
self.flow_field.apply_ddf()
temp_states = np.array(self.fifo_states)
self.force_norm_fact = 6 * np.max(np.abs(temp_states[:, 6:12]))
for i in range(6):
self.sens_deviation[i] = np.mean(temp_states[:, i])
self.sens_norm_fact[i] = 5 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i]))
for i in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.array([0.0, 0.0, 0.0, 0.0, -4*U0, 4*U0], dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy())
self.save_states = self.fifo_states.copy()
self.flow_field.apply_ddf()
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (
action,
type(action),
)
# barrier = threading.Barrier(2)
result_queue = queue.Queue()
def run_flow_field(action):
self.flow_field.context.push()
U0 = config_field.velocity
try:
temp = np.zeros(6, dtype=DATA_TYPE)
temp[3:6] = np.array((action*4+[0,-4,4])*U0, dtype=DATA_TYPE)
self.flow_field.run(SAMPLE_INTERVAL, temp)
finally:
self.flow_field.context.pop()
# barrier.wait()
self.fifo_states.append(self.flow_field.obs.copy())
def proc_data():
states = np.array(self.fifo_states)
forces = states[-1, 6:12] / self.force_norm_fact
cd = (forces[0] + forces[2] + forces[4]) / 3
cl = (forces[1] + forces[3] + forces[5]) / 3
sens = (states[-1, 0:6] - self.sens_deviation) / self.sens_norm_fact
similarities = 0.0
def calc_sim(target, state):
n = len(target)
m = len(state)
dtw_matrix = np.full((n + 1, m + 1), np.inf)
dtw_matrix[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(target[i - 1] - state[j - 1])
last_min = min(dtw_matrix[i - 1, j],
dtw_matrix[i, j - 1],
dtw_matrix[i - 1, j - 1])
dtw_matrix[i, j] = cost + last_min
return 1 - (dtw_matrix[n, m] / len(target))
for i in range(0, 6):
target_seq = np.roll(self.target_states[-CONV_LEN:, i], -self.current_step-1)
state_seq = states[-CONV_LEN:, i]
similarities += calc_sim(target_seq, state_seq) / 6
self.reward_cd = np.exp(-np.abs(cd * 20))
self.reward_cl = np.exp(-np.abs(cl * 80))
self.reward_sim = np.exp(-10*np.abs(similarities - 1))
reward = np.minimum(0.2 * self.reward_cd + 0.3 * self.reward_cl + 0.5 * self.reward_sim, 1.0)
# barrier.wait()
result_queue.put((np.hstack([forces, sens]), reward))
run_flow_field(action)
proc_data()
observation, reward = result_queue.get()
truncated = bool(np.any(observation > 1) or np.any(observation < -1))
observation = np.clip(observation, -1, 1)
self.current_step += 1
done = self.current_step >= MAX_STEPS
return observation, float(reward), done, truncated, {}
def reset(self, seed=None):
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
self.fifo_states = self.save_states.copy()
self.current_step = 0
return np.zeros(S_DIM, dtype=np.float32), {}
def render(self, mode="human"):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
speed = np.sqrt(ux**2 + uy**2)
plt.figure(figsize=(10, 5))
plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY])
plt.colorbar(label='Speed')
plt.title('Scalar Velocity Field')
plt.xlabel('X')
plt.ylabel('Y')
plt.tight_layout()
plt.show()
def save_field(self, filename):
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
self.flow_field.get_ddf()
ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
with open(os.path.join(parent_dir, "output", filename), "w") as f:
f.write("Title= \"LBM 2D\"\r\n")
f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
for j in range(NY):
for i in range(NX):
f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
def close(self):
self.flow_field.__del__()
src/drl_pinball/legacy_env/legacy_karman_env.py
+518
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@@ -0,0 +1,518 @@
# drl_pinball/legacy_env/legacy_karman_env.py
"""
Standalone Karman cloak re100 environment using LegacyCelerisLab.
This module exactly reproduces env_karman_cloak_standard.py but exposes
ALL intermediate data for validation against the new CelerisLab API.
Usage::
from legacy_karman_env import legacy_build_re100, legacy_infer_re100
data = legacy_build_re100(device_id=0)
# data['target_states'], data['norm'], data['flow_field'], ...
results = legacy_infer_re100(data['flow_field'], model, data['target_states'], data['norm'], n_steps=50)
# results['sensors'], results['forces'], results['obs'], results['actions'], results['rewards']
"""
from __future__ import annotations
import os
import sys
from collections import deque
from typing import Any, Dict, Optional, Tuple
import numpy as np
# Add repo root for LegacyCelerisLab import
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from LegacyCelerisLab import FlowField # noqa: E402
from LegacyCelerisLab import utils as legacy_utils # noqa: E402
# ---------------------------------------------------------------------------
# Constants — matching env_karman_cloak_standard.py EXACTLY
# ---------------------------------------------------------------------------
CONFIG_DIR = os.path.join(_REPO, "configs", "legacy_configs")
S_DIM, A_DIM = 12, 3
U0 = 0.01
T0 = 1000
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
CONV_LEN = 30
MAX_STEPS = 500
DATA_TYPE = np.float32
ACTION_SMOOTH_WEIGHT = 0.1 # legacy run() uses internal exponential smoothing
# Geometry constants (in lattice units)
L0 = 20.0
# Disturbance cylinder (id=0 in env order)
DIST_CENTER = (10.0 * L0, None, 0.0) # y will be set at runtime
DIST_RADIUS = 1.0 * L0
# Sensors (ids=1,2,3)
SENSOR_RADIUS = L0 / 4.0
# Pinball cylinders (ids=4,5,6)
PINBALL_RADIUS = L0 / 2.0
FRONT_CENTER = (30.0 * L0, None, 0.0) # y = CENTER_Y
BOTTOM_CENTER = (31.3 * L0, None, 0.0) # y = CENTER_Y - 0.75*L0
TOP_CENTER = (31.3 * L0, None, 0.0) # y = CENTER_Y + 0.75*L0
def _center_y(ff: FlowField) -> float:
"""Return the center y-coordinate of the flow field."""
return (ff.FIELD_SHAPE[1] - 1) / 2.0
def _fill_y(cfg: Tuple[float, Optional[float], float], cy: float) -> tuple:
"""Replace None y with actual center y."""
x, y, z = cfg
if y is None:
y = cy
return (x, y, z)
# ---------------------------------------------------------------------------
# Phase 1a: Build reference dataset (reproduces env.__init__)
# ---------------------------------------------------------------------------
def legacy_build_re100(
device_id: int = 0,
viscosity: Optional[float] = None,
) -> Dict[str, Any]:
"""Reproduce env_karman_cloak_standard.__init__() exactly.
Parameters
----------
device_id : int
GPU device ID.
viscosity : float, optional
Override viscosity (default: 0.004 for Re=100).
Returns
-------
dict with keys:
flow_field : FlowField — the CFD instance (state at end of init)
target_states : ndarray (FIFO_LEN, 6) — target sensor signals
norm : dict with force_norm_fact, sens_deviation, sens_norm_fact
config : dict with all runtime parameters
fifo_after_bias : deque — FIFO state after bias rollout
"""
# Default viscosity for Re=100 (code Re, using 2D reference)
if viscosity is None:
viscosity = 0.004
# Load legacy configs
cuda_cfg = legacy_utils.load_cuda_config(
os.path.join(CONFIG_DIR, "config_cuda.json")
)
field_cfg = legacy_utils.load_flow_field_config(
os.path.join(CONFIG_DIR, "config_flowfield.json")
)
# Override viscosity
field_cfg = field_cfg._replace(viscosity=float(viscosity))
# -- Step 0: Create FlowField ------------------------------------------
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy = _center_y(ff)
NX = ff.FIELD_SHAPE[0]
NY = ff.FIELD_SHAPE[1]
# -- Step 1: Add disturbance cylinder + sensors -------------------------
# Order matters for obs indexing: dist_cyl(0), sensor0(1), sensor1(2), sensor2(3)
ff.add_cylinder(_fill_y(DIST_CENTER, cy), DIST_RADIUS)
for y_off in [2.0, 0.0, -2.0]:
sc = (40.0 * L0, cy + y_off * L0, 0.0)
ff.add_sensor(sc, SENSOR_RADIUS)
n_obj_phase1 = ff.obs.size // 2 # 4 objects
assert n_obj_phase1 == 4, f"Expected 4 objects after sensors, got {n_obj_phase1}"
# -- Step 2: Stabilize --------------------------------------------------
stabilize_steps = int(4 * NX / U0)
ff.run(stabilize_steps, np.zeros(n_obj_phase1, dtype=DATA_TYPE))
# -- Step 3: Record target signals --------------------------------------
target_states = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj_phase1, dtype=DATA_TYPE))
new_state = ff.obs.copy()[2:8] # sensors only skip dist_cyl
target_states = np.vstack((target_states, new_state))
# -- Step 4: Add pinball cylinders (ids=4,5,6) -------------------------
ff.add_cylinder(_fill_y(FRONT_CENTER, cy), PINBALL_RADIUS)
ff.add_cylinder(_fill_y(BOTTOM_CENTER, cy - 0.75 * L0), PINBALL_RADIUS)
ff.add_cylinder(_fill_y(TOP_CENTER, cy + 0.75 * L0), PINBALL_RADIUS)
n_obj_total = ff.obs.size // 2 # 7 objects
assert n_obj_total == 7, f"Expected 7 objects, got {n_obj_total}"
# -- Step 5: Stabilize with pinball -------------------------------------
ff.run(stabilize_steps, np.zeros(n_obj_total, dtype=DATA_TYPE))
# -- Step 6: Checkpoint DDF (steady pinball + disturbance state) --------
ff.get_ddf()
ff.save_ddf()
# -- Step 7: Zero-action norm collection --------------------------------
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj_total, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[2:14]) # sensor[6] + force[6]
temp_states = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp_states[:, 6:12])))
sens_deviation = np.mean(temp_states[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp_states[:, i] - sens_deviation[i])))
# -- Step 8: Bias-action rollout (for FIFO init in controlled runs) -----
ff.apply_ddf() # restore pre-bias state
# Action bias: front=0, bottom=-4*U0, top=4*U0
bias_arr = np.zeros(n_obj_total, dtype=DATA_TYPE)
bias_arr[n_obj_total - 3] = float((0.0 * 8.0 + 0.0) * U0) # front = 0
bias_arr[n_obj_total - 2] = float((0.0 * 8.0 + (-4.0)) * U0) # bottom = -4*U0
bias_arr[n_obj_total - 1] = float((0.0 * 8.0 + 4.0) * U0) # top = 4*U0
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[2:14])
save_states = np.array(list(fifo), dtype=DATA_TYPE)
# -- Step 9: Restore to steady state (ready for reset) ------------------
ff.apply_ddf()
norm = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
"save_states": save_states,
"action_bias": [0.0, -4.0, 4.0],
"n_obj_total": n_obj_total,
}
config = {
"device_id": device_id,
"viscosity": viscosity,
"u0": U0,
"sample_interval": SAMPLE_INTERVAL,
"fifo_len": FIFO_LEN,
"conv_len": CONV_LEN,
"nx": NX,
"ny": NY,
"n_obj_total": n_obj_total,
"action_scale": 8.0,
"action_bias": [0.0, -4.0, 4.0],
}
return {
"flow_field": ff,
"target_states": target_states,
"norm": norm,
"config": config,
"fifo_after_bias": fifo,
}
# ---------------------------------------------------------------------------
# Phase 1b: Inference — reproduces env.step() exactly
# ---------------------------------------------------------------------------
def legacy_infer_re100(
flow_field: FlowField,
model: Any,
target_states: np.ndarray,
norm: Dict[str, Any],
n_steps: int = 50,
*,
use_deterministic: bool = True,
) -> Dict[str, np.ndarray]:
"""Run Karman cloak re100 controlled inference with legacy CFD.
This follows the exact pattern in env_karman_cloak_standard.step() and
analysis_crossre/scripts/phase1_infer.py.
Parameters
----------
flow_field : FlowField
Initialized flow field (must be in steady pinball state).
model : PPO
Trained PPO model (with Sin activation).
target_states : ndarray (FIFO_LEN, 6)
Target sensor signals.
norm : dict
Normalization factors from legacy_build_re100().
n_steps : int
Number of inference steps (each = SAMPLE_INTERVAL LBM steps).
use_deterministic : bool
Use deterministic action (True) or stochastic (False).
Returns
-------
dict with keys:
sensors : (n_steps, 6) raw sensor velocities
forces : (n_steps, 6) raw forces (all 6 force components)
obs : (n_steps, 12) normalised DRL observations
actions : (n_steps, 3) normalised DRL actions in [-1, 1]
rewards : (n_steps,) step rewards
reward_cd : (n_steps,)
reward_cl : (n_steps,)
reward_sim : (n_steps,)
similarities : (n_steps,) DTW similarity scores
"""
n_obj_total = norm.get("n_obj_total", 7)
action_scale = 8.0
action_bias = np.array(norm.get("action_bias", [0.0, -4.0, 4.0]), dtype=np.float32)
force_norm_fact = float(norm["force_norm_fact"])
sens_deviation = np.array(norm["sens_deviation"], dtype=np.float32)
sens_norm_fact = np.array(norm["sens_norm_fact"], dtype=np.float32)
# Restore steady state
flow_field.restore_ddf()
flow_field.apply_ddf()
# Bias-action FIFO init (reproduces env.__init__ bias rollout)
fifo = deque(maxlen=FIFO_LEN)
bias_arr = np.zeros(n_obj_total, dtype=DATA_TYPE)
bias_arr[n_obj_total - 3] = float(action_bias[0] * U0)
bias_arr[n_obj_total - 2] = float(action_bias[1] * U0)
bias_arr[n_obj_total - 1] = float(action_bias[2] * U0)
for _ in range(FIFO_LEN):
flow_field.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(flow_field.obs.copy()[2:14])
# Inference loop
sens_list, forc_list, obs_list = [], [], []
action_list, reward_list = [], []
reward_cd_list, reward_cl_list, reward_sim_list = [], [], []
sim_list = []
obs = np.zeros(S_DIM, dtype=np.float32)
for step in range(n_steps):
# --- PPO action ---
action, _states = model.predict(obs, deterministic=use_deterministic)
action = action.astype(np.float32).flatten()
action_list.append(action.copy())
# --- Convert to legacy action array ---
action_arr = np.zeros(n_obj_total, dtype=DATA_TYPE)
omega = (action * action_scale + action_bias) * U0
action_arr[n_obj_total - 3:] = omega
# --- Run CFD ---
# Context management: push/pop to avoid PyTorch CUDA context conflicts
flow_field.context.push()
try:
flow_field.run(SAMPLE_INTERVAL, action_arr)
finally:
flow_field.context.pop()
# --- Read telemetry ---
obs_slice = flow_field.obs.copy()[2:14]
fifo.append(obs_slice)
sens_list.append(obs_slice[0:6].copy())
forc_list.append(obs_slice[6:12].copy())
# --- Build normalised observation ---
forces_norm = obs_slice[6:12] / force_norm_fact
sens_norm = (obs_slice[0:6] - sens_deviation) / sens_norm_fact
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
obs_list.append(obs)
# --- Compute reward (exactly matching env logic) ---
states_arr = np.array(fifo, dtype=np.float32)
if len(states_arr) >= CONV_LEN:
forces = states_arr[-1, 6:12] / force_norm_fact
cd = float((forces[0] + forces[2] + forces[4]) / 3.0)
cl = float((forces[1] + forces[3] + forces[5]) / 3.0)
# Similarity = lag-compensated DTW over all 6 sensor channels
# calc_lag on middle sensor (index 1 = sensor1_uy)
target_seq = target_states[CONV_LEN:2 * CONV_LEN, 1]
state_seq = states_arr[-CONV_LEN:, 1]
lag = _calc_lag(target_seq, state_seq)
sim_sum = 0.0
for i in range(6):
t_seq = np.roll(target_states[:, i], -lag)[CONV_LEN:2 * CONV_LEN]
s_seq = states_arr[-CONV_LEN:, i]
sim_sum += _calc_dtw_sim(t_seq, s_seq) / 6.0
similarities = float(sim_sum)
sim_list.append(similarities)
r_cd = float(np.exp(-abs(cd * 20.0)))
r_cl = float(np.exp(-abs(cl * 80.0)))
r_sim = float(np.exp(-10.0 * abs(similarities - 1.0)))
reward = float(min(0.3 * r_cd + 0.4 * r_cl + 0.3 * r_sim, 1.0))
else:
reward = 0.0
r_cd = 0.0
r_cl = 0.0
r_sim = 0.0
similarities = 0.0
reward_list.append(reward)
reward_cd_list.append(r_cd)
reward_cl_list.append(r_cl)
reward_sim_list.append(r_sim)
return {
"sensors": np.array(sens_list, dtype=np.float32),
"forces": np.array(forc_list, dtype=np.float32),
"obs": np.array(obs_list, dtype=np.float32),
"actions": np.array(action_list, dtype=np.float32),
"rewards": np.array(reward_list, dtype=np.float32),
"reward_cd": np.array(reward_cd_list, dtype=np.float32),
"reward_cl": np.array(reward_cl_list, dtype=np.float32),
"reward_sim": np.array(reward_sim_list, dtype=np.float32),
"similarities": np.array(sim_list, dtype=np.float32) if sim_list else np.zeros(n_steps, dtype=np.float32),
}
# ---------------------------------------------------------------------------
# Helper: uncontrolled rollout (zero action)
# ---------------------------------------------------------------------------
def legacy_uncontrolled_re100(
flow_field: FlowField,
n_steps: int = 50,
) -> Dict[str, np.ndarray]:
"""Run uncontrolled Karman cloak re100 inference.
Parameters
----------
flow_field : FlowField
Must be in saved DDF state (steady pinball + disturbance).
n_steps : int
Number of steps.
Returns
-------
dict with sensors, forces.
"""
n_obj_total = 7
flow_field.restore_ddf()
flow_field.apply_ddf()
sens_list, forc_list = [], []
for _ in range(n_steps):
flow_field.run(SAMPLE_INTERVAL, np.zeros(n_obj_total, dtype=DATA_TYPE))
obs_slice = flow_field.obs.copy()[2:14]
sens_list.append(obs_slice[0:6].copy())
forc_list.append(obs_slice[6:12].copy())
return {
"sensors": np.array(sens_list, dtype=np.float32),
"forces": np.array(forc_list, dtype=np.float32),
}
# ---------------------------------------------------------------------------
# DTW helpers (exact copies from env_karman_cloak_standard.py)
# ---------------------------------------------------------------------------
def _calc_lag(target: np.ndarray, state: np.ndarray) -> int:
target_mean = float(np.mean(target))
state_mean = float(np.mean(state))
correlation = np.correlate(
target - target_mean,
state - state_mean,
mode="full",
)
lags = np.arange(-len(target) + 1, len(target))
return int(lags[np.argmax(correlation)])
def _calc_dtw_sim(target: np.ndarray, state: np.ndarray) -> float:
n = len(target)
m = len(state)
dtw_matrix = np.full((n + 1, m + 1), np.inf)
dtw_matrix[0, 0] = 0.0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(float(target[i - 1]) - float(state[j - 1]))
last_min = min(
dtw_matrix[i - 1, j],
dtw_matrix[i, j - 1],
dtw_matrix[i - 1, j - 1],
)
dtw_matrix[i, j] = cost + last_min
return float(1.0 - dtw_matrix[n, m] / n)
# ---------------------------------------------------------------------------
# CLI entry point for reference dataset generation
# ---------------------------------------------------------------------------
def main():
"""Generate reference dataset for Karman re100 validation."""
import argparse
import json
ap = argparse.ArgumentParser(description="Legacy Karman re100 reference dataset")
ap.add_argument("--device", type=int, default=0, help="GPU device ID")
ap.add_argument("--out", type=str, default="output/validate_re100", help="Output directory")
args = ap.parse_args()
out_dir = os.path.abspath(args.out)
os.makedirs(out_dir, exist_ok=True)
print("=== Building legacy Karman re100 reference dataset ===")
print(f"Output: {out_dir}")
# Build env
data = legacy_build_re100(device_id=args.device)
ff = data["flow_field"]
# Save target and norm
np.savez(os.path.join(out_dir, "target.npz"),
target_states=data["target_states"])
norm_json = {
"force_norm_fact": float(data["norm"]["force_norm_fact"]),
"sens_deviation": list(float(x) for x in data["norm"]["sens_deviation"]),
"sens_norm_fact": list(float(x) for x in data["norm"]["sens_norm_fact"]),
"action_bias": data["norm"]["action_bias"],
}
with open(os.path.join(out_dir, "norm.json"), "w") as f:
json.dump(norm_json, f, indent=2)
np.savez(os.path.join(out_dir, "save_states.npz"),
save_states=data["norm"]["save_states"])
# Save config
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump({k: str(v) if isinstance(v, (np.integer, np.floating)) else v
for k, v in data["config"].items()}, f, indent=2)
# Uncontrolled rollout
print(" uncontrolled rollout (50 steps)...")
unc = legacy_uncontrolled_re100(ff, n_steps=50)
np.savez(os.path.join(out_dir, "uncontrolled.npz"),
sensors=unc["sensors"], forces=unc["forces"])
print(" Reference dataset saved.")
print(f" force_norm_fact = {norm_json['force_norm_fact']:.6f}")
print(f" sens_deviation = {norm_json['sens_deviation']}")
print(f" sens_norm_fact = {norm_json['sens_norm_fact']}")
# Cleanup
del ff
print("Done.")
if __name__ == "__main__":
main()
src/drl_pinball/legacy_test/paraview.ipynb
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src/drl_pinball/legacy_train/erase.py
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# 训练d1a3o12_250729_250326_erase系列模型
# 上游扰流圆柱，场景与Karman_cloak_standard一致，
# 但是目标是希望pinball后流场跟入口一致，即抹除扰流圆柱尾迹
# 模型名中D代表信号延迟
# erase模型类似re100，其余模型基于erase模型迁移训练
import os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
os.environ["OMP_NUM_THREADS"] = "8"
os.environ["MKL_NUM_THREADS"] = "8"
import torch
import numpy as np
from torch.nn import Module
import gymnasium as gym
from legacy_env.legacy_env_erase import CustomEnv
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.vec_env import DummyVecEnv
from sb3_contrib import RecurrentPPO
from torch.utils.tensorboard import SummaryWriter
import pickle
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
class Sin(Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(x)
if __name__ == '__main__':
vec_env = CustomEnv(device_id=2)
name = "d1a3o12_250729_250326_erase_250804_20D_retrain2"
model = PPO.load(os.path.join(parent_dir, "models", "250729", "d1a3o12_250729_250326_erase_250804_20D.zip"), env=vec_env, device=torch.device("cuda:2"))
# model = PPO(
# "MlpPolicy",
# policy_kwargs=dict(activation_fn=Sin),
# env=vec_env,
# device=torch.device("cuda:1"),
# # n_steps=3000,
# # batch_size=300,
# verbose=0)
writer = SummaryWriter(log_dir=os.path.join(parent_dir, "tensorboard", name))
max_reward = 0
history_data = []
for i in range(500):
model.learn(total_timesteps=400)
test_env = model.get_env()
test_obs = test_env.reset()
list_reward = []
episolde_data = {'actions': [], 'observations': [], 'rewards': []}
for step in range(200):
test_action, _states = model.predict(observation=test_obs)
test_obs, test_rewards, test_dones, info = test_env.step(test_action)
list_reward.append(test_rewards)
episolde_data['actions'].append(test_action[0, :])
episolde_data['observations'].append(np.array(test_obs))
episolde_data['rewards'].append(test_rewards)
history_data.append(episolde_data)
avg_reward = np.mean(list_reward[-100:])
writer.add_scalar('Reward', np.mean(avg_reward), i)
if avg_reward > max_reward:
max_reward = avg_reward
model.save(os.path.join(parent_dir, "models", "250729", name + ".zip"))
# if i % 10 == 0:
# model.save(os.path.join(parent_dir, "models", "250329", name + f"_{i}.zip"))
# with open(os.path.join(parent_dir, "output", name + ".pkl"), 'wb') as f:
# pickle.dump(history_data, f)
src/drl_pinball/legacy_train/imit.py
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# 训练d1a3o14_250525_imit系列模型
# 上游干净来流，目标是pinball后流场跟设定尺寸圆柱一致
# 模型名中L代表目标直径，S代表SAMPLE_INTERVAL
import os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
os.environ["OMP_NUM_THREADS"] = "8"
os.environ["MKL_NUM_THREADS"] = "8"
import torch
import numpy as np
from torch.nn import Module
import gymnasium as gym
from legacy_env.legacy_env_imit import CustomEnv
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.vec_env import DummyVecEnv
from sb3_contrib import RecurrentPPO
from torch.utils.tensorboard import SummaryWriter
import pickle
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
class Sin(Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(x)
if __name__ == '__main__':
vec_env = CustomEnv(device_id=1)
name = "d1a3o14_250525_imit_1L_2U_1000S_08Vis"
model = PPO.load(os.path.join(parent_dir, "models", "250525", "d1a3o14_250525_imit_1L_2U_600S"), env=vec_env, device=torch.device("cuda:1"))
# model = PPO(
# "MlpPolicy",
# policy_kwargs=dict(activation_fn=Sin),
# env=vec_env,
# device=torch.device("cuda:2"),
# # n_steps=3000,
# # batch_size=300,
# verbose=0)
writer = SummaryWriter(log_dir=os.path.join(parent_dir, "tensorboard", name))
max_reward = 0
history_data = []
for i in range(500):
model.learn(total_timesteps=400)
test_env = model.get_env()
test_obs = test_env.reset()
list_reward = []
episolde_data = {'actions': [], 'observations': [], 'rewards': []}
for step in range(300):
test_action, _states = model.predict(observation=test_obs)
test_obs, test_rewards, test_dones, info = test_env.step(test_action)
list_reward.append(test_rewards)
episolde_data['actions'].append(test_action[0, :])
episolde_data['observations'].append(np.array(test_obs))
episolde_data['rewards'].append(test_rewards)
history_data.append(episolde_data)
avg_reward = np.mean(list_reward[-100:])
writer.add_scalar('Reward', np.mean(avg_reward), i)
if avg_reward > max_reward:
max_reward = avg_reward
model.save(os.path.join(parent_dir, "models", "250525", name + ".zip"))
# if i % 10 == 0:
# model.save(os.path.join(parent_dir, "models", "250421", name + f"_{i}.zip"))
with open(os.path.join(parent_dir, "output", name + ".pkl"), 'wb') as f:
pickle.dump(history_data, f)
src/drl_pinball/legacy_train/karman_cloak.py
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# 训练d1a3o12_250326模型，用于训练和评估d1a3o12_re系列模型和250326模型
# re100应等同于250326模型，不同雷诺数使用不同粘性实现
# re系列都基于re100模型迁移训练，250326模型直接训练
import os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
os.environ["OMP_NUM_THREADS"] = "8"
os.environ["MKL_NUM_THREADS"] = "8"
import torch
import numpy as np
from torch.nn import Module
import gymnasium as gym
from legacy_env.legacy_env_karman_cloak_standard import CustomEnv
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.vec_env import DummyVecEnv
from sb3_contrib import RecurrentPPO
from torch.utils.tensorboard import SummaryWriter
import pickle
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
class Sin(Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(x)
if __name__ == '__main__':
vec_env = CustomEnv(device_id=3)
name = "d1a3o12_250326"
# model = PPO.load(os.path.join(parent_dir, "models", "d1a3o12_c0"), env=vec_env, device=torch.device("cuda:1"))
model = PPO(
"MlpPolicy",
policy_kwargs=dict(activation_fn=Sin),
env=vec_env,
device=torch.device("cuda:3"),
# n_steps=3000,
# batch_size=300,
verbose=0)
writer = SummaryWriter(log_dir=os.path.join(parent_dir, "tensorboard", name))
max_reward = 0
history_data = []
for i in range(500):
model.learn(total_timesteps=360)
test_env = model.get_env()
test_obs = test_env.reset()
list_reward = []
episolde_data = {'actions': [], 'observations': [], 'rewards': []}
for step in range(360):
test_action, _states = model.predict(observation=test_obs)
test_obs, test_rewards, test_dones, info = test_env.step(test_action)
list_reward.append(test_rewards)
episolde_data['actions'].append(test_action[0, :])
episolde_data['observations'].append(np.array(test_obs))
episolde_data['rewards'].append(test_rewards)
history_data.append(episolde_data)
avg_reward = np.mean(list_reward[-180:])
writer.add_scalar('Reward', np.mean(avg_reward), i)
if avg_reward > max_reward:
max_reward = avg_reward
model.save(os.path.join(parent_dir, "models", "250326", name + ".zip"))
# if i % 10 == 0:
# model.save(os.path.join(parent_dir, "models", "250326", name + f"_{i}.zip"))
with open(os.path.join(parent_dir, "output", name + ".pkl"), 'wb') as f:
pickle.dump(history_data, f)
src/drl_pinball/legacy_train/reduce_obs.py
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# 训练d1a3o12_250421系列模型
# 上游扰流圆柱，场景与Karman_cloak_standard一致
# obs从12逐渐减少至2，观察模型是否能够适应，具体观察量在模型名中体现
import os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
os.environ["OMP_NUM_THREADS"] = "8"
os.environ["MKL_NUM_THREADS"] = "8"
import torch
import numpy as np
from torch.nn import Module
import gymnasium as gym
from legacy_env.legacy_env_reduce_obs import CustomEnv
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.vec_env import DummyVecEnv
from sb3_contrib import RecurrentPPO
from torch.utils.tensorboard import SummaryWriter
import pickle
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
class Sin(Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(x)
if __name__ == '__main__':
vec_env = CustomEnv(device_id=3)
name = "d1a3o12_250421_total_force"
# model = PPO.load(os.path.join(parent_dir, "models", "d1a3o12_c0"), env=vec_env, device=torch.device("cuda:1"))
model = PPO(
"MlpPolicy",
policy_kwargs=dict(activation_fn=Sin),
env=vec_env,
device=torch.device("cuda:3"),
# n_steps=3000,
# batch_size=300,
verbose=0)
writer = SummaryWriter(log_dir=os.path.join(parent_dir, "tensorboard", name))
max_reward = 0
history_data = []
for i in range(500):
model.learn(total_timesteps=400)
test_env = model.get_env()
test_obs = test_env.reset()
list_reward = []
episolde_data = {'actions': [], 'observations': [], 'rewards': []}
for step in range(300):
test_action, _states = model.predict(observation=test_obs)
test_obs, test_rewards, test_dones, info = test_env.step(test_action)
list_reward.append(test_rewards)
episolde_data['actions'].append(test_action[0, :])
episolde_data['observations'].append(np.array(test_obs))
episolde_data['rewards'].append(test_rewards)
history_data.append(episolde_data)
avg_reward = np.mean(list_reward[-100:])
writer.add_scalar('Reward', np.mean(avg_reward), i)
if avg_reward > max_reward:
max_reward = avg_reward
model.save(os.path.join(parent_dir, "models", "250421", name + ".zip"))
# if i % 10 == 0:
# model.save(os.path.join(parent_dir, "models", "250421", name + f"_{i}.zip"))
with open(os.path.join(parent_dir, "output", name + ".pkl"), 'wb') as f:
pickle.dump(history_data, f)
src/drl_pinball/legacy_train/vortex.py
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# 训练vortex模型，基于d1a3o12_re100模型，训练vortex_taylor和vortex_lamb模型
# 上游干净来流，目标是vortex流过的时序信号于无pinball情况一致
# vortes系列模型都基于d1a3o12_re100模型迁移训练
import os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
os.environ["OMP_NUM_THREADS"] = "16"
os.environ["MKL_NUM_THREADS"] = "16"
import torch
import numpy as np
from torch.nn import Module
import gymnasium as gym
from legacy_env.legacy_env_vortex import CustomEnv
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.vec_env import DummyVecEnv
from sb3_contrib import RecurrentPPO
from torch.utils.tensorboard import SummaryWriter
import pickle
current_dir = os.path.dirname(os.path.abspath("__file__"))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
class Sin(Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(x)
if __name__ == '__main__':
vec_env = CustomEnv(device_id=3)
name = "vortex_taylor"
model = PPO.load(os.path.join(parent_dir, "models", "d1a3o12_re100"), env=vec_env, device=torch.device("cuda:3"))
# model = PPO(
# "MlpPolicy",
# policy_kwargs=dict(activation_fn=Sin),
# env=vec_env,
# device=torch.device("cuda:3"),
# n_steps=3600,
# batch_size=360,
# verbose=0)
writer = SummaryWriter(log_dir=os.path.join(parent_dir, "tensorboard", name))
max_reward = 0
history_data = []
for i in range(100):
model.learn(total_timesteps=1500)
test_env = model.get_env()
test_obs = test_env.reset()
list_reward = []
# episolde_data = {'actions': [], 'observations': [], 'rewards': []}
for step in range(150):
test_action, _states = model.predict(observation=test_obs)
test_obs, test_rewards, test_dones, info = test_env.step(test_action)
list_reward.append(test_rewards)
# episolde_data['actions'].append(test_action[0, :])
# episolde_data['observations'].append(np.array(test_obs))
# episolde_data['rewards'].append(test_rewards)
# history_data.append(episolde_data)
avg_reward = np.mean(list_reward[-130:])
writer.add_scalar('Reward', np.mean(avg_reward), i)
if avg_reward > max_reward:
max_reward = avg_reward
model.save(os.path.join(parent_dir, "models", name + ".zip"))
# with open(os.path.join(parent_dir, "output", name + ".pkl"), 'wb') as f:
# pickle.dump(history_data, f)
src/environments/__init__.py
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"""
Gymnasium environments for CFD control tasks.
"""
from .cfd_env import CFDFlowControlEnv
__all__ = ['CFDFlowControlEnv']
src/environments/cfd_env.py
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@@ -1,328 +0,0 @@
"""
CFD Flow Control Environment using CelerisLab.
A Gymnasium environment for active flow control using lattice Boltzmann simulation.
"""
import os
from collections import deque
from typing import Optional, Tuple, Dict, Any
import gymnasium as gym
import numpy as np
from gymnasium import spaces
# Set threading to avoid conflicts with GPU
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
class CFDFlowControlEnv(gym.Env):
"""
CFD flow control environment with cylinder and sensors.
The environment simulates flow around a cylinder with multiple control cylinders
and sensors to measure flow properties. The agent controls the cylinder velocities
to optimize flow characteristics.
Args:
device_id: CUDA device ID to use for simulation
config_cuda: CelerisLab CUDA configuration (optional, will load from config if None)
config_field: CelerisLab flow field configuration (optional, will load from config if None)
n_control_cylinders: Number of controllable cylinders (default: 3)
n_sensors: Number of flow sensors (default: 3)
max_steps: Maximum steps per episode (default: 500)
sample_interval: Simulation steps between observations (default: 800)
fifo_length: Length of state history (default: 120)
convergence_length: Steps to check for convergence (default: 60)
warmup_steps_factor: Multiple of grid size for warmup (default: 4)
"""
metadata = {"render_modes": ["human"], "render_fps": 30}
def __init__(
self,
device_id: int = 0,
config_cuda = None,
config_field = None,
n_control_cylinders: int = 3,
n_sensors: int = 3,
max_steps: int = 500,
sample_interval: int = 800,
fifo_length: int = 120,
convergence_length: int = 60,
warmup_steps_factor: int = 4,
):
super().__init__()
# Load configurations if not provided
if config_cuda is None or config_field is None:
from ..config import load_celeris_configs
config_cuda, config_field = load_celeris_configs()
self.config_cuda = config_cuda
self.config_field = config_field
self.device_id = device_id
# Environment parameters
self.n_control = n_control_cylinders
self.n_sensors = n_sensors
self.max_steps = max_steps
self.sample_interval = sample_interval
self.fifo_length = fifo_length
self.convergence_length = convergence_length
self.warmup_steps_factor = warmup_steps_factor
# Determine data type
if config_field.data_type == "FP32":
self.dtype = np.float32
elif config_field.data_type == "FP64":
self.dtype = np.float64
else:
raise ValueError(f"Unsupported data type: {config_field.data_type}")
# Action and observation dimensions
# Action: velocity control for n cylinders (x, y, rotation)
self.action_dim = n_control_cylinders
# Observation: sensor readings (u, v) from n sensors
self.obs_dim = n_sensors * 2 * 2 # 2 velocity components × 2 (current + derivative)
# Gym spaces
self.action_space = spaces.Box(
low=-1.0,
high=1.0,
shape=(self.action_dim,),
dtype=self.dtype
)
self.observation_space = spaces.Box(
low=-np.inf,
high=np.inf,
shape=(self.obs_dim,),
dtype=self.dtype
)
# State tracking
self.fifo_states = deque(maxlen=fifo_length)
self.target_states = np.empty((0, self.n_sensors * 2), dtype=self.dtype)
self.current_step = 0
# Normalization factors (will be set during warmup)
self.sens_norm_fact = np.ones(self.n_sensors * 2, dtype=self.dtype)
self.sens_deviation = np.zeros(self.n_sensors * 2, dtype=self.dtype)
# Reward tracking
self.reward_cd = 0.0
self.reward_cl = 0.0
self.reward_sim = 0.0
# Initialize flow field
self._init_flow_field()
def _init_flow_field(self):
"""Initialize the CelerisLab flow field simulation."""
from CelerisLab import FlowField
self.flow_field = FlowField(
self.config_field,
self.config_cuda,
self.device_id
)
# Get grid parameters
L0 = 20 # Characteristic length
U0 = self.config_field.velocity
NX = self.flow_field.FIELD_SHAPE[0]
NY = self.flow_field.FIELD_SHAPE[1]
# Add main cylinder (obstacle)
center = (10 * L0, (NY - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0)
# Add sensors
sensor_y_positions = [
(NY - 1) / 2 + 2 * L0, # Above centerline
(NY - 1) / 2, # At centerline
(NY - 1) / 2 - 2 * L0, # Below centerline
]
for i in range(min(self.n_sensors, len(sensor_y_positions))):
center = (40 * L0, sensor_y_positions[i], 0)
self.flow_field.add_sensor(center, L0 / 4)
# Warmup simulation
warmup_steps = int(self.warmup_steps_factor * NX / U0)
self.flow_field.run(warmup_steps, np.zeros(self.n_control + 1, dtype=self.dtype))
# Collect baseline states for normalization
for _ in range(self.fifo_length):
self.flow_field.run(
self.sample_interval,
np.zeros(self.n_control + 1, dtype=self.dtype)
)
new_state = self.flow_field.obs.copy()[2:2 + self.n_sensors * 2]
self.target_states = np.vstack((self.target_states, new_state))
self.fifo_states.append(new_state)
# Calculate normalization factors
self._calculate_normalization()
def _calculate_normalization(self):
"""Calculate normalization factors from baseline states."""
if len(self.target_states) > 0:
self.sens_norm_fact = np.std(self.target_states, axis=0) + 1e-6
self.sens_deviation = np.mean(self.target_states, axis=0)
def _normalize_state(self, state: np.ndarray) -> np.ndarray:
"""Normalize state using calculated factors."""
return (state - self.sens_deviation) / self.sens_norm_fact
def _compute_reward(self, state: np.ndarray, action: np.ndarray) -> float:
"""
Compute reward based on drag reduction and flow similarity.
Args:
state: Current state observation
action: Applied action
Returns:
Total reward
"""
# Get force measurements from simulation
obs = self.flow_field.obs
cd = obs[0] # Drag coefficient
cl = obs[1] # Lift coefficient
# Drag reduction reward (negative drag is good)
self.reward_cd = -cd * 0.1
# Lift minimization (want symmetric flow)
self.reward_cl = -abs(cl) * 0.05
# Flow similarity to baseline (want smooth control)
if len(self.fifo_states) >= self.convergence_length:
recent_states = np.array(list(self.fifo_states)[-self.convergence_length:])
target_recent = self.target_states[-self.convergence_length:]
# Dynamic Time Warping distance (simplified)
diff = np.mean(np.abs(recent_states - target_recent))
self.reward_sim = -diff * 0.5
else:
self.reward_sim = 0.0
# Total reward
total_reward = self.reward_cd + self.reward_cl + self.reward_sim
return float(total_reward)
def reset(
self,
seed: Optional[int] = None,
options: Optional[Dict[str, Any]] = None
) -> Tuple[np.ndarray, Dict[str, Any]]:
"""
Reset the environment to initial state.
Args:
seed: Random seed for reproducibility
options: Additional options
Returns:
Tuple of (observation, info)
"""
super().reset(seed=seed)
self.current_step = 0
self.fifo_states.clear()
# Run a few steps to get initial state
for _ in range(10):
self.flow_field.run(
self.sample_interval,
np.zeros(self.n_control + 1, dtype=self.dtype)
)
state = self.flow_field.obs.copy()[2:2 + self.n_sensors * 2]
self.fifo_states.append(state)
# Get current state
current_state = self.fifo_states[-1]
# Compute state derivative (approximation)
if len(self.fifo_states) >= 2:
state_derivative = self.fifo_states[-1] - self.fifo_states[-2]
else:
state_derivative = np.zeros_like(current_state)
# Normalize and concatenate
obs = np.concatenate([
self._normalize_state(current_state),
self._normalize_state(state_derivative)
]).astype(self.dtype)
info = {
'step': self.current_step,
'cd': self.flow_field.obs[0],
'cl': self.flow_field.obs[1],
}
return obs, info
def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]:
"""
Take a step in the environment.
Args:
action: Action to take (cylinder velocities)
Returns:
Tuple of (observation, reward, terminated, truncated, info)
"""
# Convert action to control input
# Action is in [-1, 1], scale to appropriate velocity range
control = np.zeros(self.n_control + 1, dtype=self.dtype)
control[:self.n_control] = action * 0.1 * self.config_field.velocity
# Run simulation
self.flow_field.run(self.sample_interval, control)
# Get new state
new_state = self.flow_field.obs.copy()[2:2 + self.n_sensors * 2]
self.fifo_states.append(new_state)
# Compute observation
if len(self.fifo_states) >= 2:
state_derivative = self.fifo_states[-1] - self.fifo_states[-2]
else:
state_derivative = np.zeros_like(new_state)
obs = np.concatenate([
self._normalize_state(new_state),
self._normalize_state(state_derivative)
]).astype(self.dtype)
# Compute reward
reward = self._compute_reward(new_state, action)
# Check termination
self.current_step += 1
terminated = False # CFD simulations typically don't have natural termination
truncated = self.current_step >= self.max_steps
# Info
info = {
'step': self.current_step,
'cd': float(self.flow_field.obs[0]),
'cl': float(self.flow_field.obs[1]),
'reward_cd': float(self.reward_cd),
'reward_cl': float(self.reward_cl),
'reward_sim': float(self.reward_sim),
}
return obs, reward, terminated, truncated, info
def render(self):
"""Render the environment (not implemented)."""
pass
def close(self):
"""Clean up resources."""
if hasattr(self, 'flow_field'):
del self.flow_field