A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
Wu, Ling; Nguyen, Van Dung; Kilingar, Nanda Gopalaet al.
2020 • In Computer Methods in Applied Mechanics and Engineering, 369, p. 113234
[en] An artificial Neural Network (NNW) is designed to serve as a surrogate model of micro-scale simulations in the context of multi-scale analyzes 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 allows 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 FE2 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.
Research Center/Unit :
A&M - Aérospatiale et Mécanique - ULiège
Disciplines :
Mechanical engineering Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Wu, Ling ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Nguyen, Van Dung ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Kilingar, Nanda Gopala ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Noels, Ludovic ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Language :
English
Title :
A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
Publication date :
01 September 2020
Journal title :
Computer Methods in Applied Mechanics and Engineering
ISSN :
0045-7825
eISSN :
1879-2138
Publisher :
Elsevier, Amsterdam, Netherlands
Volume :
369
Pages :
113234
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
H2020 - 862015 - MOAMMM - Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials
Name of the research project :
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015 for the project Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM) of the H2020-EU.1.2.1. - FET Open Programme
Funders :
EC - European Commission F.R.S.-FNRS - Fonds de la Recherche Scientifique European Union
Commentary :
Data can be downloaded on https://gitlab.uliege.be/moammm/moammmPublic/publicationsData/2020_CMAME_RNN or https://doi.org/10.5281/zenodo.3902663
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