相关实验视频
基于不变网络的格子博尔兹曼方法的机器学习增强碰撞运算符
Mario Christopher Bedrunka1, Tobias Horstmann2, Ben Picard3
1Bonn-Rhein-Sieg University of Applied Sciences, University of Siegen, Chair of Fluid Mechanics, Paul-Bonatz-Straße 9-11, 57076 Siegen-Weidenau, Germany and Institute of Technology, Resource and Energy-efficient Engineering (TREE), Grantham-Allee 20, 53757 Sankt Augustin, Germany.
Physical review. E
|December 23, 2025
概括
机器学习通过优化格子博尔茨曼方法来增强计算流体动力学模拟.
科学领域:
- 计算流体动力学的流体动力学.
- 机器学习 机器学习
- 数字模拟的数字模拟.
背景情况:
- 格子博尔茨曼法 (LBM) 是流体动力学的数值解法.
- 将机器学习 (ML) 集成到LBM中可以提高准确性和稳定性.
- 碰撞操作员是ML集成到LBM的关键组件.
研究的目的:
- 开发一个新的神经碰撞操作员 (NCO) 用于LBM.
- 使用ML提高LBM模拟的稳定性和准确性.
- 优化非物理时刻的放松率,以提高稳定性.
主要方法:
- 构建了一个不变的神经网络,在等价碰撞运算机上起作用.
- 训练了NCO使用强制的同otropic流模拟与光谱强迫.
- 通过定制的损失函数,最大限度地降低了能量频谱差异和量身定制的数值消散.
主要成果:
- 与BGK和KBC运营商相比,NCO表现出更好的准确性和稳定性.
- 精确预测极低分辨率的三维泰勒-格林 (TGV) 流动中的动态.
- 在动荡的三维流模拟中强大的性能.
结论:
- 该NCO提供了一种有前途的方法来增强LBM模拟.
- 将ML集成到LBM碰撞操作员中可以获得更准确,更稳定的结果.
- 替代训练程序使高雷诺兹数模拟能够在减少内存足迹的情况下进行.
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