Hybrid data-driven closure strategies for reduced order modeling
Hybrid data-driven closure strategies for reduced order modeling
复制标题
用于降阶建模的混合数据驱动闭合策略
DOI：
10.1016/j.amc.2023.127920
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发表时间：
2023
影响因子：
4
通讯作者：
Rozza, Gianluigi
中科院分区：
文献类型：
--
作者：
Ivagnes, Anna;Stabile, Giovanni;Mola, Andrea;Iliescu, Traian;Rozza, Gianluigi
在本文中，我们提出了混合数据驱动的ROM封闭的流体流动。这些新的ROM闭合联合收割机结合了两种根本不同的策略：（i）纯数据驱动的ROM闭合，用于速度和压力;以及（ii）基于物理的涡流粘度数据驱动闭合，其模拟系统中的能量传递。第一种策略是在控制方程中加入封闭/校正项，这些方程是根据现有数据建立的。第二种策略包括通过添加涡流粘度项来进行湍流建模，涡流粘度项是通过使用机器学习技术来确定的。本文首次将这两种方法结合起来研究Re = 50，000时二维圆柱绕流问题。我们的数值结果表明，混合数据驱动ROM是更准确的比纯粹的数据驱动ROM和涡粘性ROM。
In this paper, we propose hybrid data-driven ROM closures for fluid flows. These new ROM closures combine two fundamentally different strategies:(i) purely data-driven ROM closures, both for the velocity and the pressure; and (ii) physically based, eddy viscosity data-driven closures, which model the energy transfer in the system. The first strategy consists in the addition of closure/correction terms to the governing equations, which are built from the available data. The second strategy includes turbulence modeling by adding eddy viscosity terms, which are determined by using machine learning techniques. The two strategies are combined for the first time in this paper to investigate a two-dimensional flow past a circular cylinder at R e= 50, 000. Our numerical results show that the hybrid data-driven ROM is more accurate than both the purely data-driven ROM and the eddy viscosity ROM.