ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity

TL;DR

This paper introduces an ANN-assisted incremental multiscale remodelling approach for finite strain poroelasticity, enabling accurate, efficient modeling of complex micro-macro interactions in large deformation scenarios.

Contribution

It presents a novel ANN-based framework that accounts for micro-macro property interdependencies during finite strain remodelling, improving predictive accuracy over traditional models.

Findings

Enhanced accuracy in fluid-saturated porous media modeling.

Captured nonlinear deviations from Darcy's law.

Simulated brain tissue response under cyclic loading.

Abstract

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 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 uniaxial cyclic test is simulated and studied.