Biomechanical characterization of the passive response of the thoracic aorta in chronic hypoxic newborn lambs using an evolutionary strategy

Abstract

The present study involves experiments and modelling aimed at characterizing the passive structural mechanical behavior of the chronic hypoxic lamb thoracic aorta, whose gestation, birth and postnatal period were carried at high altitude (3600 masl). To this end, the mechanical response was studied via tensile and pressurization tests. The tensile and pressurization tests measurements were used simultaneously to calibrate the material parameters of the Gasser–Holzapfel–Ogden (GHO) hyperelasctic anisotropic constitutive model through an analytical-numerical optimization procedure solved with an evolutionary strategy that guarantees a stable response of the model. The model and procedure of calibration adequately adjust to the material behavior in a wide deformation range with an appropriate physical description. The results of this study predict the mechanical response of the lamb thoracic aorta under generalized loading states like those that can occur in physiological conditions and/or in systemic arterial hypertension. Finally, the novel use of the evolutionary strategy, together with the set of experiments and tools used in this study, provide a robust alternative to validate biomechanical characterizations.

Through the cleared aorta: three-dimensional characterization of mechanical behaviors of rat thoracic aorta under intraluminal pressurization using optical clearing method

“…4 ). Such mechanical behavior under intraluminal pressurization was also reported in a previous study using the same artery from the same animal model 24 as well as in the same and other arteries in other species 25 , 26 . The coincidence with other studies validates not only the mechanical data obtained in the present study but also the use of the current optical clearing method for dynamic two-photon microscopy experiment.…”

An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta

“…The procedure described below considers the experimental data of a descending thoracic aorta of a 30-day-old newborn lamb ( Ovis Aries ) available in the works of ( Rivera et al, 2020 ; Rivera et al, 2021 ). Further information about protocols and methods of measurement are detailed in both references.…”

“…The physiological arterial stretch λ z related to in-vivo and zero-pressure states (steps (1) and (2), respectively) was obtained via the pre-stretching test, measuring the longitudinal shortening between the zero-pressure and ex-vivo states (steps (2) and (3)) ( Navarrete et al, 2020 ). The outer diameters and were registered from in-vitro inflation-extension tests ( Rivera et al, 2021 ), where was considered under the reference pressure value, which in this case was set as the diastolic pressure ( p = 10 kPa ( Maes et al, 2019 )), and was measured at zero pressure. The experimental data of the inner and outer diameters in ex-vivo state (step (3)) was obtained from images of ring shape specimens.…”

“…To achieve this objective, the CMM has been implemented in the FEBio finite element framework ( Maas et al, 2012 ). The model characterization has been performed using experimental mechanical data reported by ( Rivera et al, 2021 ) on a newborn lamb-specific descending thoracic aorta. The CMM is fitted to fulfill and correlate with experimental data obtained from the following tests: ring-opening, pre-stretch, pressurization-stretch and uniaxial tensile using samples in circumferential and longitudinal directions.…”

Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

“…However, the number of error functional evaluations entailed by the optimization process does not seem to be a critical issue in the hyperelastic materials identification literature, and such information was not very often provided in technical papers . Nevertheless, the selected implementations of some metaheuristic algorithms (e.g., particle swarm optimization used in [67], standard simulated annealing used in [83] and evolution strategies used in [85,86]) inherently entailed a huge number of finite element analyses (i.e., 10,000 for [67], about 10,000 for [83] and 30,000 to 170,000 for [85,86]) to solve the identification problem.…”