Multi-compartment poroelastic models of perfused biological soft tissues: implementation in FEniCSx

TL;DR

This paper develops and implements multi-compartment poro-elastic models of soft tissues in FEniCSx, demonstrating accurate results and improved computational efficiency over previous methods, with applications to biological tissue mechanics.

Contribution

It provides a comprehensive framework for modeling poro-elasticity in FEniCSx, including theory, implementation, and validation against benchmarks, enhancing accessibility and performance.

Findings

Accurate modeling of poro-elastic tissues with benchmark validation.

FEniCSx implementation is three times faster than legacy FEniCS.

Parallel computation improves simulation efficiency.

Abstract

Soft biological tissues demonstrate strong time-dependent and strain-rate mechanical behavior, arising from their intrinsic visco-elasticity and fluid-solid interactions (especially at sufficiently large time scales). The time-dependent mechanical properties of soft tissues influence their physiological functions and are linked to several pathological processes. Poro-elastic modeling represents a promising approach because it allows the integration of multiscale/multiphysics data to probe biologically relevant phenomena at a smaller scale and embeds the relevant mechanisms at the larger scale. The implementation of multi-phasic flow poro-elastic models however is a complex undertaking, requiring extensive knowledge. The open-source software FEniCSx Project provides a novel tool for the automated solution of partial differential equations by the finite element method. This paper aims to provide the required tools to model the mixed formulation of poro-elasticity, from the theory to the implementation, within FEniCSx. Several benchmark cases are studied. A column under confined compression conditions is compared to the Terzaghi analytical solution, using the L2-norm. An implementation of poro-hyper-elasticity is proposed. A bi-compartment column is compared to previously published results (Cast3m implementation). For all cases, accurate results are obtained in terms of a normalized Root Mean Square Error (RMSE). Furthermore, the FEniCSx computation is found three times faster than the legacy FEniCS one. The benefits of parallel computation are also highlighted.