Mesh sampling and weighting for the hyperreduction of nonlinear Petrov–Galerkin reduced‐order models with local reduced‐order bases
Abstract
The energy‐conserving sampling and weighting (ECSW) method is a hyper‐reduction 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 hyper‐reduction 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(107) and O(108) 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 11, 2021
- Source ID
- 10.1002/nme.6603
Entities
People
- Charbel Bou‐mosleh
- Charbel Farhat
- Radek Tezaur
- Sebastian Grimberg
Organizations
- Air Force Office of Scientific Research
- Boeing
- King Abdulaziz City for Science and Technology
- Office of Naval Research
- Stanford University
- University of Notre Dame