论文标题

非线性模型还原:Pod-Galerkin和POD-DEIM方法之间的比较

Nonlinear model reduction: a comparison between POD-Galerkin and POD-DEIM methods

论文作者

Sipp, Denis, de Pando, Miguel Fosas, Schmid, Peter J.

论文摘要

比较了库拉莫托 - sivashinsky方程的三种非平行版本的三个案例,比较了几种非线性模型的技术,这是$ re = 100 $的圆柱体的瞬态流程,并在相同的雷诺数上以圆柱体超过圆柱体。盖勒金(Galerkin)投影在流量状态的POD基础上缩小了管理方程式的线性项,而降低的非线性对流项则通过Galerkin投射到同一状态的基础上,通过Galerkin的投影在POD基础上获得的POD基础或代表非线性的POD基础,或者通过代表非线性interpolation interpolation(deim)将其应用于podeare podeal code,从而获得了。评估了减少订单模型的质量,以介绍其稳定性,准确性和鲁棒性,并引入并进行了适当的定量措施。特别是,将简化的线性项的性质与全尺度项的属性进行了比较,并且分析了非线性二次术语的结构,分析了动能的保护。结果表明,所有三种还原技术都为库拉莫托 - 西瓦辛斯基方程和极限循环缸流的情况提供了极好的相似结果。对于经过圆柱体的流动状态的瞬态状态,只有纯盖尔金技术是成功的,而Deim技术会产生降低的模型,这些模型在有限的时间内会发散。

Several nonlinear model reduction techniques are compared for the three cases of the non-parallel version of the Kuramoto-Sivashinsky equation, the transient regime of flow past a cylinder at $Re=100$ and fully developed flow past a cylinder at the same Reynolds number. The linear terms of the governing equations are reduced by Galerkin projection onto a POD basis of the flow state, while the reduced nonlinear convection terms are obtained either by a Galerkin projection onto the same state basis, by a Galerkin projection onto a POD basis representing the nonlinearities or by applying the Discrete Empirical Interpolation Method (DEIM) to a POD basis of the nonlinearities. The quality of the reduced order models is assessed as to their stability, accuracy and robustness, and appropriate quantitative measures are introduced and compared. In particular, the properties of the reduced linear terms are compared to those of the full-scale terms, and the structure of the nonlinear quadratic terms is analyzed as to the conservation of kinetic energy. It is shown that all three reduction techniques provide excellent and similar results for the cases of the Kuramoto-Sivashinsky equation and the limit-cycle cylinder flow. For the case of the transient regime of flow past a cylinder, only the pure Galerkin techniques are successful, while the DEIM technique produces reduced-order models that diverge in finite time.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源