239 lines
9.9 KiB
Python
239 lines
9.9 KiB
Python
import gymnasium as gym
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import numpy as np
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from gymnasium import spaces
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import ctypes
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from collections import deque
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from typing import Tuple
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import sys
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import os
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import matplotlib.pyplot as plt
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import queue
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os.environ["OMP_NUM_THREADS"] = "1"
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os.environ["MKL_NUM_THREADS"] = "1"
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current_dir = os.path.dirname(os.path.abspath("__file__"))
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parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
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sys.path.append(parent_dir)
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from CelerisLab import FlowField
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from CelerisLab import utils
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config_cuda = utils.load_cuda_config(
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os.path.join(parent_dir, "configs", "config_cuda.json")
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)
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config_field = utils.load_flow_field_config(
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os.path.join(parent_dir, "configs", "config_flowfield.json")
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)
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S_DIM, A_DIM = 12, 3
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U0 = config_field.velocity
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T0 = 1000
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SAMPLE_INTERVAL = 800
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FIFO_LEN = 150
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CONV_LEN = 36
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MAX_STEPS = 150
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L0 = 20
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if config_field.data_type == "FP32":
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DATA_TYPE = np.float32
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else:
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raise ValueError(f"Unsupported data type {config_field.data_type}.")
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class CustomEnv(gym.Env):
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"""Custom Environment that follows gym interface."""
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metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL}
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def __init__(self, device_id=0):
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super().__init__()
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self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
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self.observation_space = spaces.Box(
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low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE
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)
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self.fifo_states = deque(maxlen=FIFO_LEN)
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self.target_states = np.empty((0, 6), dtype=DATA_TYPE)
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self.force_norm_fact = 1.0
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self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
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self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
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self.reward_cd = 0.0
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self.reward_cl = 0.0
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self.reward_sim = 0.0
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self.current_step = 0
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self.flow_field = FlowField(config_field, config_cuda, device_id)
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U0 = config_field.velocity
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NX = self.flow_field.FIELD_SHAPE[0]
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NY = self.flow_field.FIELD_SHAPE[1]
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self.center_vor: Tuple[float, float, float] = (15 * L0, (NY - 1) / 2 - 0*L0, 0)
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center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 + 2 * L0, 0)
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self.flow_field.add_sensor(center, L0 / 4)
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center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2, 0)
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self.flow_field.add_sensor(center, L0 / 4)
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center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 - 2 * L0, 0)
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self.flow_field.add_sensor(center, L0 / 4)
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self.flow_field.run(int(4*NX/U0), np.zeros(3, dtype=DATA_TYPE))
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self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.5*U0, 0, "lamb")
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self.flow_field.get_ddf()
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for i in range(FIFO_LEN):
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self.flow_field.run(SAMPLE_INTERVAL, np.zeros(3, dtype=DATA_TYPE))
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new_state = self.flow_field.obs.copy()
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# if i == 150:
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# self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.5*U0, 0, "lamb")
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# self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.03*U0, 0, "taylor")
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self.target_states = np.vstack((self.target_states, new_state))
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self.flow_field.apply_ddf()
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center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0)
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self.flow_field.add_cylinder(center, L0 / 2)
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center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0)
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self.flow_field.add_cylinder(center, L0 / 2)
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center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0)
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self.flow_field.add_cylinder(center, L0 / 2)
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self.flow_field.run(int(2*NX/U0), np.zeros(6, dtype=DATA_TYPE))
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self.flow_field.run(int(2*NX/U0), np.array([0.0, 0.0, 0.0, 0.0, -5*U0, 5*U0], dtype=DATA_TYPE))
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self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.5*U0, 0, "lamb")
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self.flow_field.get_ddf()
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self.flow_field.save_ddf()
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for i in range(FIFO_LEN):
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self.flow_field.run(SAMPLE_INTERVAL, np.zeros(6, dtype=DATA_TYPE))
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self.fifo_states.append(self.flow_field.obs.copy())
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# if i == 150:
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# self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.5*U0, 0, "lamb")
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# self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.03*U0, 0, "taylor")
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temp_states = np.array(self.fifo_states)
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self.force_norm_fact = 6 * np.max(np.abs(temp_states[:, 6:12]))
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# temp_states = np.vstack((temp_states[:, 0:6], self.target_states))
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for i in range(6):
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self.sens_deviation[i] = np.mean(temp_states[:, i])
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self.sens_norm_fact[i] = 5 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i]))
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self.flow_field.apply_ddf()
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# for i in range(FIFO_LEN):
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# self.flow_field.run(SAMPLE_INTERVAL, np.zeros(6, dtype=DATA_TYPE))
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# self.fifo_states.append(self.flow_field.obs.copy())
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# self.flow_field.get_ddf()
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# self.flow_field.save_ddf()
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self.save_states = self.fifo_states.copy()
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def step(self, action):
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assert self.action_space.contains(action), "%r (%s) invalid" % (
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action,
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type(action),
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)
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result_queue = queue.Queue()
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def run_flow_field(action):
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self.flow_field.context.push()
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U0 = config_field.velocity
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try:
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temp = np.zeros(6, dtype=DATA_TYPE)
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temp[3:6] = np.array((action*4+[0,-4,4])*U0, dtype=DATA_TYPE)
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self.flow_field.run(SAMPLE_INTERVAL, temp)
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finally:
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self.flow_field.context.pop()
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self.fifo_states.append(self.flow_field.obs.copy())
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def proc_data():
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states = np.array(self.fifo_states)
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forces = states[-1, 6:12] / self.force_norm_fact
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cd = (forces[0] + forces[2] + forces[4]) / 3
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cl = (forces[1] + forces[3] + forces[5]) / 3
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sens = (states[-1, 0:6] - self.sens_deviation) / self.sens_norm_fact
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similarities = 0.0
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def calc_sim(target, state):
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n = len(target)
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m = len(state)
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dtw_matrix = np.full((n + 1, m + 1), np.inf)
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dtw_matrix[0, 0] = 0
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for i in range(1, n + 1):
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for j in range(1, m + 1):
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cost = abs(target[i - 1] - state[j - 1])
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last_min = min(dtw_matrix[i - 1, j],
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dtw_matrix[i, j - 1],
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dtw_matrix[i - 1, j - 1])
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dtw_matrix[i, j] = cost + last_min
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return 1 - (dtw_matrix[n, m] / len(target))
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for i in range(0, 6):
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target_seq = np.roll(self.target_states[-CONV_LEN:, i], -self.current_step-1)
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state_seq = states[-CONV_LEN:, i]
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similarities += calc_sim(target_seq, state_seq) / 6
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self.reward_cd = np.exp(-np.abs(cd * 20))
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self.reward_cl = np.exp(-np.abs(cl * 80))
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self.reward_sim = np.exp(-10*np.abs(similarities - 1))
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reward = np.minimum(0.2 * self.reward_cd + 0.3 * self.reward_cl + 0.5 * self.reward_sim, 1.0)
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result_queue.put((np.hstack([forces, sens]), reward))
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run_flow_field(action)
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proc_data()
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observation, reward = result_queue.get()
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if self.current_step == 150:
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self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.5*U0, 0, "lamb")
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# self.flow_field.add_vortex(self.center_vor, L0 * 2, 0.03*U0, 0, "taylor")
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# truncated = bool(np.any(observation > 1) or np.any(observation < -1))
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truncated = False
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observation = np.clip(observation, -1, 1)
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self.current_step += 1
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done = self.current_step >= MAX_STEPS
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# done = False
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return observation, float(reward), done, truncated, {}
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def reset(self, seed=None):
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self.flow_field.restore_ddf()
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self.flow_field.apply_ddf()
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self.fifo_states = self.save_states.copy()
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self.current_step = 0
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return np.zeros(S_DIM, dtype=np.float32), {}
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def render(self, mode="human"):
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NX = self.flow_field.FIELD_SHAPE[0]
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NY = self.flow_field.FIELD_SHAPE[1]
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self.flow_field.get_ddf()
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ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
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ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
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uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
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speed = np.sqrt(ux**2 + uy**2)
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plt.figure(figsize=(10, 5))
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plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY])
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plt.colorbar(label='Speed')
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plt.title('Scalar Velocity Field')
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.tight_layout()
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plt.show()
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def save_field(self, filename):
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NX = self.flow_field.FIELD_SHAPE[0]
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NY = self.flow_field.FIELD_SHAPE[1]
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self.flow_field.get_ddf()
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ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0)
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flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0)
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ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0
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uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0
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with open(os.path.join(parent_dir, "output", filename), "w") as f:
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f.write("Title= \"LBM 2D\"\r\n")
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f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n")
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f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n")
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for j in range(NY):
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for i in range(NX):
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f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n")
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def close(self):
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self.flow_field.__del__() |