本研究旨在确定能够有效模拟冲击载荷作用下脑组织力学特性的粘性–超弹性本构方程。本文运用有限元仿真与优化算法相结合的方法，开展了脑组织粘性–超弹性材料模型参数求解。首先，基于脑组织动态单轴拉伸试验数据，建立最大拉伸率为 1.3、应变率分别为 30 s–1 和 90 s–1 的脑组织动态拉伸有限元仿真模型。然后，以仿真预测的工程应力–应变曲线与参考试验测量结果均值曲线的拟合误差最小化作为优化设计的目标函数，利用多目标遗传算法进行材料模型参数求解。结果显示，运用本文所确定的本构方程的脑组织有限元模型能够准确地预测不同加载速率下的脑组织动态拉伸力学特性。应用本文获取的脑组织粘性–超弹性本构方程于颅脑有限元模型，将有利于提高模型在动态冲击载荷下的生物逼真度。 The objective of this study was to determine the visco-hyperelastic constitutive law of brain tissue under dynamic impacts. A method combined by finite element simulations and optimization algorithm was employed for the determination of material variables. Firstly, finite element simulations of brain tissue dynamic uniaxial tension, with a maximum stretch rate of 1.3 and strain rates of 30 s–1 and 90 s–1, were developed referring to experimental data. Then, fitting errors between the engineering stress-strain curves predicted by simulations and experimental average curves were assigned as objective functions, and the multi-objective genetic algorithm was employed for the optimation solution. The results demonstrate that the brain tissue finite element models assigned with the novel obtained visco-hyperelastic material law could predict the brain tissue’s dynamic mechanical characteristic well at different loading rates. Meanwhile, the novel material law could also be applied in the human head finite element models for the improvement of the biofidelity under dynamic impact loadings.
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