arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.02891math.NAcs.LGcs.NA
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局部降阶基非线性Petrov-Galerkin降阶模型超约化的网格采样和加权

Mesh sampling and weighting for the hyperreduction of nonlinear Petrov-Galerkin reduced-order models with local reduced-order bases

Stanford University(斯坦福大学);Notre Dame University-Louaize(黎巴嫩圣母大学)

Sebastian Grimberg, Charbel Farhat, Radek Tezaur, Charbel Bou-Mosleh

摘要:

能量守恒采样和加权(ECSW)方法是一种超约化方法,最初是为了加速与大规模有限元模型相关的基于伽辽金投影的降阶模型(PROM)的性能而开发的,当底层投影算子需要频繁地重新计算时,如在参数和/或非线性问题中。在本文中,这种hyperreduction方法扩展到彼得罗夫-伽辽金PROM的基础高维模型可以与任意有限元,有限体积,有限差分半离散化方法。它的范围也扩展到覆盖局部PROM的分段仿射近似子空间的基础上,如那些旨在减轻Kolmogorov $n$宽度障碍问题与对流为主的流动问题。本文所得到的ECSW方法是强大的和准确的。特别是,它的离线阶段被证明是快速和可并行的,其在线阶段的工业相关性的大规模应用的潜力被证明为湍流问题与$O(10^7)$和$O(10^8)$自由度。对于这样的问题,在线部分的ECSW方法,本文提出的彼得罗夫-伽辽金PROM显示,使挂钟时间和CPU时间加速因子的几个数量级,同时提供卓越的精度。

英文摘要:

The energy-conserving sampling and weighting (ECSW) method is a hyperreduction method originally developed for accelerating the performance of Galerkin projection-based reduced-order models (PROMs) associated with large-scale finite element models, when the underlying projected operators need to be frequently recomputed as in parametric and/or nonlinear problems. In this paper, this hyperreduction method is extended to Petrov-Galerkin PROMs where the underlying high-dimensional models can be associated with arbitrary finite element, finite volume, and finite difference semi-discretization methods. Its scope is also extended to cover local PROMs based on piecewise-affine approximation subspaces, such as those designed for mitigating the Kolmogorov $n$-width barrier issue associated with convection-dominated flow problems. The resulting ECSW method is shown in this paper to be robust and accurate. In particular, its offline phase is shown to be fast and parallelizable, and the potential of its online phase for large-scale applications of industrial relevance is demonstrated for turbulent flow problems with $O(10^7)$ and $O(10^8)$ degrees of freedom. For such problems, the online part of the ECSW method proposed in this paper for Petrov-Galerkin PROMs is shown to enable wall-clock time and CPU time speedup factors of several orders of magnitude while delivering exceptional accuracy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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