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Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks

Gärtner, Til ; Fernández, Mauricio ; Weeger, Oliver (2021):
Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks. (Publisher's Version)
In: Computational Mechanics, 68 (5), pp. 1111-1130. Springer, ISSN 0178-7675, e-ISSN 1432-0924,
DOI: 10.26083/tuprints-00019875,
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Item Type: Article
Origin: Secondary publication service
Status: Publisher's Version
Title: Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks
Language: English
Abstract:

A sequential nonlinear multiscale method for the simulation of elastic metamaterials subject to large deformations and instabilities is proposed. For the finite strain homogenization of cubic beam lattice unit cells, a stochastic perturbation approach is applied to induce buckling. Then, three variants of anisotropic effective constitutive models built upon artificial neural networks are trained on the homogenization data and investigated: one is hyperelastic and fulfills the material symmetry conditions by construction, while the other two are hyperelastic and elastic, respectively, and approximate the material symmetry through data augmentation based on strain energy densities and stresses. Finally, macroscopic nonlinear finite element simulations are conducted and compared to fully resolved simulations of a lattice structure. The good agreement between both approaches in tension and compression scenarios shows that the sequential multiscale approach based on anisotropic constitutive models can accurately reproduce the highly nonlinear behavior of buckling-driven 3D metamaterials at lesser computational effort.

Journal or Publication Title: Computational Mechanics
Volume of the journal: 68
Issue Number: 5
Publisher: Springer
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
500 Naturwissenschaften und Mathematik > 530 Physik
600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 16 Department of Mechanical Engineering > Cyber-Physical Simulation (CPS)
Date Deposited: 14 Dec 2021 10:15
Last Modified: 14 Dec 2021 10:16
DOI: 10.26083/tuprints-00019875
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-198752
Additional Information:

Nonlinear multiscale simulation, Metamaterials, Constitutive modeling, Anisotropic hyperelasticity, Machine learning

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/19875
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