Article (Scientific journals)
ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity
DEHGHANI, Hamidreza; ZILIAN, Andreas
2021In Computational Mechanics
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Keywords :
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
Abstract :
[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.
Research center :
University of Luxembourg: Institute of Computational Engineering
Disciplines :
Materials science & engineering
Civil engineering
Author, co-author :
DEHGHANI, Hamidreza ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
ZILIAN, Andreas  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
External co-authors :
Language :
Title :
ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity
Publication date :
May 2021
Journal title :
Computational Mechanics
Publisher :
Springer, New York, Germany
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Physics and Materials Science
Computational Sciences
FnR Project :
FNR12252781 - Data-driven Computational Modelling And Applications, 2017 (01/09/2018-28/02/2025) - Andreas Zilian
Name of the research project :
CDE-Hub, DTU Driven
Funders :
Fonds National de la Recherche - FnR (PRIDE17/12252781)
Luxembourg Ministry of Economy (FEDER 2018-04-024)
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