Micromechanics-based surrogate models for the response of composites: A critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks

Rocha, I.B.C.M.; Kerfriden, P.; van der Meer, F.P.

doi:10.1016/j.euromechsol.2020.103995

Micromechanics-based surrogate models for the response of composites

Title

Micromechanics-based surrogate models for the response of composites: A critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks

Author

Rocha, I.B.C.M. (TU Delft Applied Mechanics)
Kerfriden, P. (Cardiff University; PSL Research University)
van der Meer, F.P. (TU Delft Applied Mechanics)

Date

2020

Abstract

Although being a popular approach for the modeling of laminated composites, mesoscale constitutive models often struggle to represent material response for arbitrary load cases. A better alternative in terms of accuracy is to use the FE² technique to upscale microscopic material behavior without loss of generality, but the associated computational effort can be extreme. It is therefore interesting to explore alternative surrogate modeling strategies that maintain as much of the fidelity of FE² as possible while still being computationally efficient. In this work, three surrogate modeling approaches are compared in terms of accuracy, efficiency and calibration effort: the state-of-the-art mesoscopic plasticity model by Vogler et al. (Vogler et al., 2013), regularized feed-forward neural networks and hyper-reduced-order models obtained by combining the Proper Orthogonal Decomposition (POD) and Empirical Cubature Method (ECM) techniques. Training datasets are obtained from a Representative Volume Element (RVE) model of the composite microstructure with a number of randomly-distributed linear-elastic fibers surrounded by a matrix with pressure-dependent plasticity. The approaches are evaluated with a comprehensive set of numerical tests comprising pure stress cases and three different stress combinations relevant in the design of laminated composites. The models are assessed on their ability to accurately reproduce the training cases as well as on how well they are able to predict unseen stress combinations. Gains in execution time are compared by using the trained surrogates in the FE² model of an interlaminar shear test.

Subject

Artificial neural networks
Hyper-reduction
Laminated composites
Reduced-order modeling

To reference this document use:

http://resolver.tudelft.nl/uuid:803a6650-4b9a-4128-a59c-245ce36e90cd

DOI

https://doi.org/10.1016/j.euromechsol.2020.103995

ISSN

0997-7538

Source

European Journal of Mechanics A - Solids, 82

Part of collection

Institutional Repository