Data -driven POD-Galerkin reduced order model for turbulent flows
Data -driven POD-Galerkin reduced order model for turbulent flows
复制标题
DOI：
10.1016/j.jcp.2020.109513
复制
发表时间：
2020-09-01
影响因子：
4.1
通讯作者：
Rozza, Gianluigi
中科院分区：
文献类型：
--
作者：
Hijazi, Saddam;Stabile, Giovanni;Rozza, Gianluigi
在这项工作中，我们提出了一个降阶模型，这是专门设计来处理湍流在有限体积设置。用于建立降阶模型的方法是基于合并/组合基于投影的技术与数据驱动的简化策略的思想。特别是，这项工作提出了一种混合策略，利用数据驱动的减少方法来近似涡粘性解流形和经典的POD-伽辽金投影方法的速度和压力场，分别。新提出的降阶模型已被验证的基准测试情况下，在稳态和非稳态设置雷诺数高达Re = O（10 5）。
In this work we present a Reduced Order Model which is specifically designed to deal with turbulent flows in a finite volume setting. The method used to build the reduced order model is based on the idea of merging/combining projection-based techniques with data-driven reduction strategies. In particular, the work presents a mixed strategy that exploits a data-driven reduction method to approximate the eddy viscosity solution manifold and a classical POD-Galerkin projection approach for the velocity and the pressure fields, respectively. The newly proposed reduced order model has been validated on benchmark test cases in both steady and unsteady settings with Reynolds up to R e= O (10 5).