Bridging Proper Orthogonal Decomposition methods and augmented Newton-Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems.
Bridging Proper Orthogonal Decomposition methods and augmented Newton-Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems.
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DOI：
10.1016/j.cma.2010.10.009
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发表时间：
2011-01-15
影响因子：
7.2
通讯作者：
Bordas S
中科院分区：
文献类型：
--
作者：
Kerfriden P;Gosselet P;Adhikari S;Bordas S
本文描述了基于POD的模型降阶技术与经典的牛顿/克里洛夫求解器之间的桥梁。这座桥被用来推导出一种有效的算法来纠正发生强烈拓扑变化的高度非线性问题的降阶建模。伤害引发的问题是通过修正的超减量方法解决的。结果表明，即使在拓扑变化较大的情况下，使用该算法也能以合理的额外代价显著提高降阶模型的相关性。
This article describes a bridge between POD-based model order reduction techniques and the classical Newton/Krylov solvers. This bridge is used to derive an efficient algorithm to correct, “on-the-fly”, the reduced order modelling of highly nonlinear problems undergoing strong topological changes. Damage initiation problems are addressed and tackle via a corrected hyperreduction method. It is shown that the relevancy of reduced order model can be significantly improved with reasonable additional costs when using this algorithm, even when strong topological changes are involved.