Reference : ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity
Scientific journals : Article
Engineering, computing & technology : Civil engineering
Engineering, computing & technology : Materials science & engineering
Physics and Materials Science; Computational Sciences
http://hdl.handle.net/10993/47065
ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity
English
Dehghani, Hamidreza mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Zilian, Andreas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
May-2021
Computational Mechanics
Springer
Yes
International
0178-7675
1432-0924
New York
Germany
[en] ANN for homogenisation and localisation ; Remodelling of multiscale and multiphysics problems ; Incremental finite strain poroelasticity ; Data-driven computational mechanics ; Deviation from Darcy’s law ; Brain tissue modelling
[en] Mechanical modelling of poroelastic media under finite strain is usually carried out via phenomenological models neglecting complex micro-macro scales interdependency. One reason is that the mathematical two-scale analysis is only straightforward assuming infinitesimal strain theory. Exploiting the potential of ANNs for fast and reliable upscaling and localisation procedures, we propose an incremental numerical approach that considers rearrangement of the cell properties based on its current deformation, which leads to the remodelling of the macroscopic model after each time increment. This computational framework is valid for finite strain and large deformation problems while it ensures infinitesimal strain increments within time steps. The full effects of the interdependency between the properties and response of macro and micro scales are considered for the first time providing a more accurate predictive analysis of fluid-saturated porous media which is studied via a numerical consolidation example. Furthermore, the (nonlinear) deviation from Darcy’s law is captured in fluid filtration numerical analyses. Finally, the brain tissue mechanical response under the uniaxial cyclic test is simulated and studied.
University of Luxembourg: Institute of Computational Engineering
Fonds National de la Recherche - FnR (PRIDE17/12252781) ; Luxembourg Ministry of Economy (FEDER 2018-04-024)
CDE-Hub, DTU Driven
Researchers ; Professionals
http://hdl.handle.net/10993/47065
10.1007/s00466-021-02023-3
https://doi.org/10.1007/s00466-021-02023-3
FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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