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深度强化学习的应用,用于参数优化和流模型的精细化
1Department of Engineering, King's College London, London, WC2R 2LS, UK. zhang.zhan987@gmail.com.
Scientific reports
|July 12, 2025
概括
这项研究使用深度决定性政策梯度 (DDPG) 来优化流模型参数,以实现更准确的计算流体动力学 (CFD) 模拟. 与传统方法相比,DDPG方法显著提高了风压系数 (WPC) 预测准确度.
科学领域:
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 空气动力学 航空动力学
- 机器学习 机器学习
背景情况:
- 传统的风洞测试和空气动力学模拟的现场测量是耗时和昂贵的.
- 准确的流模型对于可靠的CFD模拟至关重要,尤其是建筑物周围复杂的风场.
- 像基因算法 (GA) 和粒子群优化 (PSO) 这样的现有优化方法在效率和准确性方面存在局限性.
研究的目的:
- 开发和验证使用深度决定性政策梯度 (DDPG) 的流模型的新参数优化方法.
- 通过优化流模型参数,提高构建风场的计算流体动力学 (CFD) 模拟的准确性.
- 减少对昂贵和耗时的实验方法的依赖.
主要方法:
- 利用剪切应力传输 (SST) k-ω流模型作为优化基础.
- 使用OpenFOAM进行复杂建筑风场的数值模拟.
- 实施深度决定性政策梯度 (DDPG) 来优化流模型参数,使用高斯过程回归 (GPR) 作为初始CFD数据的替代模型.
- 进行了灵敏度分析以确定影响风压系数 (WPC) 模拟的关键参数.
主要成果:
- DDPG优化了流模型参数,显著提高了风压系数 (WPC) 模拟的准确性.
- 优化的WPC值 (平均值,RMS,最大值,最小值) 与单一风向角度的实际WPC数据更加一致.
- 在0°-50°风向角度范围内,DDPG优化导致模拟的WPC值更接近实际数据.
- 与GA和PSO相比,DDPG方法显示了平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 的显著减少.
结论:
- 基于DDPG的参数优化方法为CFD模拟中的流建模提供了更准确,更有效的方法.
- 这种方法有效地减少了模拟错误,并改善了建筑物周围风压系数的预测.
- DDPG优化为传统方法和其他进化算法提供了一种优越的替代方案,用于提高CFD准确性.
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