Model order reduction for large-scale structures with local nonlinearities

Zhenying Zhang*, Mengwu Guo*, Jan S. Hesthaven*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

In solid mechanics, linear structures often exhibit (local) nonlinear behavior when close to failure. For instance, the elastic deformation of a structure becomes plastic after being deformed beyond recovery. To properly assess such problems in a real-life application, we need fast and multi-query evaluations of coupled linear and nonlinear structural systems, whose approximations are not straight forward and often computationally expensive. In this work, we propose a linear–nonlinear domain decomposition, where the two systems are coupled through the solutions on a prescribed linear–nonlinear interface. After necessary sensitivity analysis, e.g. for structures with a high dimensional parameter space, we adopt a non-intrusive method, e.g. Gaussian processes regression (GPR), to solve for the solution on the interface. We then utilize different model order reduction techniques to address the linear and nonlinear problems individually. To accelerate the approximation, we employ again the non-intrusive GPR for the nonlinearity, while intrusive model order reduction methods, e.g. the conventional reduced basis (RB) method or the static-condensation reduced-basis-element (SCRBE) method, are employed for the solution in the linear subdomain. The proposed method is applicable for problems with pre-determined linear–nonlinear domain decomposition. We provide several numerical examples to demonstrate the effectiveness of our method.
Original languageEnglish
Pages (from-to)491-515
JournalComputer methods in applied mechanics and engineering
Volume353
DOIs
Publication statusPublished - Aug 2019
Externally publishedYes

Keywords

  • Model order reduction
  • Reduced basis method
  • Nonlinear structural analysis
  • Gaussian process regression
  • Machine learning

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