Inverse material characterisation of human aortic tissue for traumatic injury in motor vehicle crashes

Abstract

Traumatic aortic rupture (TAR) resulting due to motor vehicle crashes (MVC) accounts for about 10%–20% fatalities. This article presents the outcome of our findings of the non-linear stress–strain response and strain rate-dependent behaviour of the aortic tissue under crash like impacts. This study uses both finite element (FE) modelling and experimental testing to enhance the understanding of injury mechanisms associated with TAR. Accurate material properties are essential for correct FE model predictions. Therefore, the objective of the current study was to experimentally characterise and identify a suitable constitutive model and model parameters for aortic tissue that can be incorporated into FE human body models for studying aortic rupture. Inverse characterisation and genetic algorithms (GA) were used to train the FE model to simulate real-life scenarios applying loading in multiple directions. A total of 32 uniaxial tensile tests were conducted on human aortic tissues along with the longitudinal and circumferential directions by loading at different strain rates ranging up to 200/s. The engineering stress–strain relationship obtained for human aorta in the longitudinal and circumferential directions via uniaxial tensile tests demonstrated an approximately bilinear behaviour and strain rate dependency in both directions. The higher stresses and modulus in the circumferential direction as compared to longitudinal direction demonstrates the anisotropic behaviour of the tissue. A constitutive model has been developed and implemented via user subroutine (UMAT) in LS-DYNA that accounts for the strain rate-dependent effects and bilinear behaviour observed in the aortic tissue. A FE model of the experimental set-up was developed, and the parameters of the model were estimated using a GA-based inverse mapping technique. The developed model enables investigation of the mechanical response of the aortic tissue under crashes and other high rate loading conditions. The work is based on the premise that a reliable constitutive model coupled with a FE model of the aorta shall help predict TARs.