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Model order reduction for large-scale structures with local nonlinearities
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DOI：10.1016/j.cma.2019.04.042.png)
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摘要
En 中文
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. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Model order reduction
Reduced basis method
Nonlinear structural analysis
Gaussian process regression
Machine learning
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IF:
7.3
论文数:
1.3W
被引数:
5.6W
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