An open-source FEniCS-based framework for hyperelastic parameter estimation from noisy full-field data: Application to heterogeneous soft tissues

摘要

We introduce a finite-element-model-updating-based open-source framework to identify mechanical parameters of heterogeneous hyperelastic materials from in silico generated full-field data which can be downloaded here https://github.com/aflahelouneg/inverse_identification_soft_tissue. The numerical process consists in simulating an extensometer performing in vivo uniaxial tensile experiment on a soft tissue. The reaction forces and displacement fields are respectively captured by force sensor and Digital Image Correlation techniques. By means of a forward nonlinear FEM model and an inverse solver, the model parameters are estimated through a constrained optimization function with no quadratic penalty term. As a case study, our Finite Element Model Updating (FEMU) tool has been applied on a model composed of a keloid scar surrounded by healthy skin. The results show that at least 4 parameters can be accurately identified from an uniaxial test only. The originality of this work lies in two major elements. Firstly, we develop a low-cost technique able to characterize the mechanical properties of heterogeneous nonlinear hyperelastic materials. Secondly, we explore the model accuracy via a detailed study of the interplay between discretization error and the error due to measurement uncertainty. Next steps consist in identifying the real parameters and so finding the matching preferential directions of keloid scars growth. (c) 2021 Published by Elsevier Ltd.