188 lines
7.5 KiB
Python
188 lines
7.5 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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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 = 120
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CONV_LEN = 60
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MAX_STEPS = 240
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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.force_norm_fact = 1.0
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self.sens_norm_fact = 1.0
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self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
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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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L0 = 20
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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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center: Tuple[float, float, float] = (30 * 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] = (30 * 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] = (30 * 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] = (20 * 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] = (21.3 * 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] = (21.3 * 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(4*NX/U0), np.zeros(6, dtype=DATA_TYPE))
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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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# temp_states = np.array(self.fifo_states)
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# self.force_norm_fact = 3 * np.max(np.abs(temp_states[:, 6:12]))
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# for i in range(3):
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# self.sens_deviation[2*i] = np.mean(temp_states[:, 2*i])
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# # self.sens_deviation[2*i] = 0.0
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# self.sens_norm_fact = 6 * np.max(np.abs(temp_states[:, 1]))
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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*8)*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.fifo_states.append(self.flow_field.obs.copy())
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self.flow_field.context.pop()
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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] * 50
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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] / 2
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def theo_velo(y):
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NY = self.flow_field.FIELD_SHAPE[1]
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U0 = config_field.velocity
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yy = (y - 0.5 * (NY - 1)) / (NY - 2.0)
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u = U0 * 1.5 * (1 - 4 * yy * yy)
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return u
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similarities = 0.0
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NY = self.flow_field.FIELD_SHAPE[1]
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L0 = 20
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sens_pos = np.array([(NY - 1) / 2 + 2 * L0, (NY - 1) / 2, (NY - 1) / 2 - 2 * L0])
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for i in range(3):
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u = theo_velo(sens_pos[i])
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similarities += np.exp(-4*np.abs(states[-1, 2*i] - u))/6 + np.exp(-8*np.abs(states[-1, 2*i+1] - 0))/6
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reward_cd = np.exp(-np.abs(cd * 10))
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reward_cl = np.exp(-np.abs(cl * 10))
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# reward_sim = np.exp(2 * (similarities - 1))
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reward_sim = similarities
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reward = np.minimum(0.4 * reward_cd + 0.6 * reward_cl + 0.0 * reward_sim, 1.0)
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# barrier.wait()
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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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truncated = bool(np.any(observation > 1) or np.any(observation < -1))
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observation = np.clip(observation, -1, 1)
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terminated = self.current_step >= MAX_STEPS
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self.current_step += 1
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return observation, float(reward), terminated, truncated, {}
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def reset(self, seed=None):
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self.flow_field.apply_ddf()
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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__() |