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使用LSTM动态调整SIMPLE算法松弛因子的CFD仿真训练程序

news 来源：原创 2025/4/21 7:57:06

使用LSTM动态调整SIMPLE算法松弛因子的CFD仿真训练程序

下面是一个基于PyTorch的示例程序，展示如何训练LSTM网络根据残差历史动态调整SIMPLE算法的松弛因子。这个程序假设您已经有一定的CFD仿真数据或能够生成合成数据用于训练。

开源资源

在开始之前，这里有一些有用的开源资源：

OpenFOAM - 开源CFD软件包 (https://openfoam.org/)
PyTorch - 深度学习框架 (https://pytorch.org/)
DeepXDE - 用于物理信息神经网络的库 (https://github.com/lululxvi/deepxde)
FluidNet - 用于流体模拟的神经网络 (https://github.com/google/FluidNet)

示例训练程序

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt# 假设的CFD仿真数据生成器（实际应用中应替换为真实数据）
class CFDSimulationDataset(Dataset):def __init__(self, num_samples=1000, sequence_length=20, num_variables=4):self.num_samples = num_samplesself.sequence_length = sequence_lengthself.num_variables = num_variables# 生成合成数据：残差历史和最佳松弛因子# 在实际应用中，这应该来自真实的CFD仿真数据self.X = np.random.randn(num_samples, sequence_length, num_variables) * 0.1self.y = np.random.rand(num_samples, 2) * 0.5 + 0.5  # 假设松弛因子在0.5-1.0之间# 添加一些简单的模式使数据更有意义for i in range(num_samples):mean_res = np.mean(self.X[i, :, 0])self.y[i, 0] = 0.7 - mean_res * 0.2  # 压力松弛因子self.y[i, 1] = 0.6 - mean_res * 0.1  # 速度松弛因子def __len__(self):return self.num_samplesdef __getitem__(self, idx):return torch.FloatTensor(self.X[idx]), torch.FloatTensor(self.y[idx])# LSTM模型定义
class RelaxationFactorPredictor(nn.Module):def __init__(self, input_size, hidden_size, num_layers, output_size):super(RelaxationFactorPredictor, self).__init__()self.hidden_size = hidden_sizeself.num_layers = num_layersself.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)self.fc = nn.Linear(hidden_size, output_size)self.sigmoid = nn.Sigmoid()def forward(self, x):# 初始化隐藏状态和细胞状态h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)# LSTM前向传播out, _ = self.lstm(x, (h0, c0))# 只取最后一个时间步的输出out = out[:, -1, :]# 全连接层out = self.fc(out)# 使用sigmoid将输出限制在0-1之间（松弛因子通常在这个范围）out = self.sigmoid(out) * 0.5 + 0.5  # 限制在0.5-1.0之间return out# 训练参数
input_size = 4  # 输入特征数（例如：压力残差、速度残差等）
hidden_size = 64
num_layers = 2
output_size = 2  # 输出：压力松弛因子和速度松弛因子
sequence_length = 20
batch_size = 32
num_epochs = 100
learning_rate = 0.001# 创建数据集和数据加载器
dataset = CFDSimulationDataset(num_samples=1000, sequence_length=sequence_length, num_variables=input_size)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)# 初始化模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RelaxationFactorPredictor(input_size, hidden_size, num_layers, output_size).to(device)# 损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)# 训练循环
loss_history = []
for epoch in range(num_epochs):for i, (sequences, targets) in enumerate(dataloader):sequences = sequences.to(device)targets = targets.to(device)# 前向传播outputs = model(sequences)loss = criterion(outputs, targets)# 反向传播和优化optimizer.zero_grad()loss.backward()optimizer.step()if (i+1) % 10 == 0:print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(dataloader)}], Loss: {loss.item():.4f}')loss_history.append(loss.item())# 绘制损失曲线
plt.plot(loss_history)
plt.xlabel('Iterations')
plt.ylabel('Loss')
plt.title('Training Loss')
plt.show()# 保存模型
torch.save(model.state_dict(), 'relaxation_factor_predictor.pth')# 示例使用：如何在实际CFD仿真中应用
def predict_relaxation_factors(residual_history):"""在实际CFD仿真中使用训练好的模型预测松弛因子参数:residual_history: numpy数组, 形状为(sequence_length, num_variables)包含最近的残差历史记录"""# 加载模型model = RelaxationFactorPredictor(input_size, hidden_size, num_layers, output_size)model.load_state_dict(torch.load('relaxation_factor_predictor.pth'))model.eval()# 准备输入数据input_data = torch.FloatTensor(residual_history).unsqueeze(0)  # 添加batch维度# 预测with torch.no_grad():predicted_factors = model(input_data)# 返回压力松弛因子和速度松弛因子return predicted_factors[0, 0].item(), predicted_factors[0, 1].item()# 示例调用
if __name__ == "__main__":# 假设我们有一些残差历史数据example_history = np.random.randn(sequence_length, input_size) * 0.1pressure_relax, velocity_relax = predict_relaxation_factors(example_history)print(f"Predicted relaxation factors - Pressure: {pressure_relax:.3f}, Velocity: {velocity_relax:.3f}")