A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths

Autor: Ling Wu, Nanda Gopala Kilingar, Ludovic Noels, Van Dung Nguyen
Přispěvatelé: Commission Européenne. Direction Générale de la Recherche et de l’Innovation - DG RDT [sponsor], Fonds de la Recherche Scientifique - F.R.S.-FNRS [sponsor], Aérospatiale et Mécanique - A&M [research center]
Rok vydání: 2020
Předmět:
Zdroj: Computer Methods in Applied Mechanics and Engineering
Open Repository and Bibliography-University of Liège
Sygma
Microsoft Academic Graph
A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths. Computer Methods in Applied Mechanics and Engineering, 369, 113234.Amsterdam, NetherlandsElsevier. (2020).
ISSN: 0045-7825
1134-3060
1097-0207
1522-2608
0927-0256
0020-7683
0021-9991
0997-7538
0955-7997
0749-6419
0045-7949
2296-8016
0027-8424
0022-5096
1432-0924
DOI: 10.1016/j.cma.2020.113234
Popis: An artificial Neural Network (NNW) is designed to serve as a surrogate model of micro-scale simulations in the context of multi-scale analyses in solid mechanics. The design and training methodologies of the NNW are developed in order to allow accounting for history-dependent material behaviors. On the one hand, a Recurrent Neural Network (RNN) using a Gated Recurrent Unit (GRU) is constructed, which allows mimicking the internal variables required to account for history-dependent behaviors since the RNN is self-equipped with hidden variables that have the ability of tracking loading history. On the other hand, in order to achieve accuracy under multi-dimensional non-proportional loading conditions, training of the RNN is achieved using sequential data. In particular the sequential training data are collected from finite element simulations on an elasto-plastic composite RVE subjected to random loading paths. The random loading paths are generated in a way similar to a random walking in stochastic process and allow generating data for a wide range of strain-stress states and state evolution. The accuracy and efficiency of the RNN-based surrogate model is tested on the structural analysis of an open-hole sample subjected to several loading/unloading cycles. It is shown that a similar accuracy as with a FE 2 multi-scale simulation can be reached with the RNN-based surrogate model as long as the local strain state remains in the training range, while the computational time is reduced by four orders of magnitude.
Databáze: OpenAIRE
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