通过XGBoost集成的CFD模拟,对液滴蒸发过程进行半替代建模
Yihuan Yan1, Xueren Li2, Weijie Sun3
1School of Air Transportation / Flying, Shanghai University of Engineering Science, Shanghai 201620, China.
The Science of the total environment
|June 25, 2023
概括
机器学习加速了计算流体动力学 (CFD) 模拟. 一个新的基于XGBoost的模型准确地预测了滴水蒸发速度比传统方法快100万倍,误差低于5%.
科学领域:
- 流体动力学 流体动力学
- 计算科学 计算科学
- 机器学习 机器学习
背景情况:
- 计算流体动力学 (CFD) 模拟对于复杂的问题至关重要,但在计算上昂贵.
- 加速这些模拟对于高效的研发至关重要.
- 滴滴蒸发是呼吸道滴滴运输中的一个关键现象,需要准确的建模.
研究的目的:
- 开发一个半替代模型,将机器学习 (ML) 与CFD集成,以加速流体动力学模拟.
- 应用该模型来预测不同室内条件下的滴水蒸发动态.
- 与传统的CFD相比,评估基于ML的方法的准确性和速度.
主要方法:
- 使用 eXtreme Gradient Boosting (XGB) 算法开发了一个半替代模型.
- 传统的欧勒尔-拉格兰治 CFD 框架被用来模拟在各种湿度和温度条件下的滴水蒸发.
- 生成的CFD数据用于训练XGB模型,以预测随时间推移的滴滴大小.
主要成果:
- 经过训练的XGB模型准确地捕获了滴滴动态和环境参数之间的非线性关系.
- 关键参数的预测错误低于5%.
- 该XGB模型实现了预测速度大约是传统CFD模拟的100万倍.
结论:
- 基于XGB的半替代模型提供了一个高效和准确的方法来预测复杂的流体动力学现象,如滴滴蒸发.
- 这种方法显著降低了重复模拟的计算成本和时间.
- 该模型的预测可以集成到新的CFD模拟中,进一步提高效率.
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