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12 April 2022
Unpublished conference/Abstract (Scientific congresses and symposiums)
Elasto-plastic multi-scale simulations accelerated by a recurrent neural network-based surrogate model
Noels, Ludovic  ; Wu, Ling  ; Nguyen, Van Dung 
2022 • 18th European Mechanics of Materials Conference (EMMC18)
 

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Keywords :
Multi-scale; Composite; Machine-learning; Neural-network; elasto-plasticity
Abstract :
[en] Computational homogenization embedded in a multi-scale analysis is a versatile tool also called the FE2 method. Although general and accurate, this methodology yields prohibitive computational time. In order to make the methodology affordable, a more efficient approach is to conduct pre-off-line finite element simulations on the mesoscale problem in order to build a synthetic data-base. The data-base can in turn be used to train a surrogate model, and once this so-called training step is completed, the surrogate model can be used as a constitutive law on a classical finite element simulation, speeding up the multi-scale process by several orders. Artificial neural networks (NNWs) offer the possibility to serve as a surrogate model, but a difficulty arises for elasto-plasticity because of its history-dependency. This difficulty can be solved by considering a Recurrent Neural Network (RNN), which uses sequential information [1]. Nevertheless, in order to be accurate under multi-dimensional non-proportional loading conditions, a sufficiently wide data-base is required in order to perform the training. To this end, a sequential training synthetic data-base is obtained from finite element simulations on an elasto-plastic composite RVE subjected to random loading paths. The RNN predictions are thus found to be in agreement with the FE2 simulations, while reducing the computational cost by 4 orders of magnitude. [1] L. Wu, V.-D. Nguyen, N.G. Kilingar and L. Noels, Computer Methods in Applied Mechanics and Engineering, 360:113234, 2020.
Research center :
A&M - Aérospatiale et Mécanique - ULiège
Disciplines :
Mechanical engineering
Materials science & engineering
Aerospace & aeronautics engineering
Author, co-author :
Noels, Ludovic  ;  Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
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)
Language :
English
Title :
Elasto-plastic multi-scale simulations accelerated by a recurrent neural network-based surrogate model
Publication date :
05 April 2022
Event name :
18th European Mechanics of Materials Conference (EMMC18)
Event organizer :
EuroMech
Event place :
Oxford, United Kingdom
Event date :
4-6 April 2022
Audience :
International
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
European Projects :
H2020 - 862015 - MOAMMM - Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials
Name of the research project :
MOAMMM
Funders :
EC - European Commission
Funding number :
862015
Funding text :
This project has received funding from the European Union´s Horizon 2020 research and innovation programme under grant agreement No. 862015 for the project “Multiscale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM)” of the H2020-EU.1.2.1. - FET Open Programme.

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