生物医学工程学杂志

生物医学工程学杂志

基于动态拉伸试验数据的脑组织粘性–超弹性材料模型参数求解

查看全文

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.

关键词: 有限元; 多目标优化; 参数反求; 超弹性; 粘弹性; 本构方程

Key words: finite element; multi-object optimization; parameter inverse; hyperelasticity; viscoelasticity; constitutive law

登录后 ,请手动点击刷新查看全文内容。 没有账号,
登录后 ,请手动点击刷新查看图表内容。 没有账号,
1. World Health Organization. Global status report on road safety 2015. Geneva: World Health Organization, 2015.
2. Coronado V G, Xu L, Basavaraju S V, et al. Surveillance for traumatic brain injury-related deaths: United States, 1997-2007. Atlanta: US Department of Health and Human Services, Centers for Disease Control and Prevention, 2011.
3. Yang Jikuang, Xu Wei, Otte D. Brain injury biomechanics in real world vehicle accident using mathematical models. Chin J Mech Eng, 2008, 21(4): 81-86.
4. 曹立波, 周舟, 蒋彬辉, 等. 10 岁儿童头部有限元模型的建立及验证. 中国生物医学工程学报, 2014, 33(1): 63-70.
5. Mao Haojie, Zhang Liying, Jiang Binhui, et al. Development of a finite element human head model partially validated with thirty five experimental cases. J Biomech Eng, 2013, 135(11): 1-15.
6. 李海岩, 赵玮, 阮世捷, 等. 第 95 百分位中国人头部颅脑相对位移的有限元评估. 医用生物力学, 2012, 27(2): 198-206.
7. Sahoo D, Deck C, Willinger R. Brain injury tolerance limit based on computation of axonal strain. Accident Analysis & Prevention, 2016, 92: 53-70.
8. Kleiven S. Predictors for traumatic brain injuries evaluated through accident reconstructions. Stapp Car Crash J, 2007, 51: 81-114.
9. Brands D W, Bovendeerd P H, Peters G W, et al. The large shear strain dynamic behaviour of in-vitro porcine brain tissue and a silicone gel model material. Stapp Car Crash J, 2000, 44: 249-260.
10. Bilston L E, Liu Z Z, Phan-Thien N. Large strain behaviour of brain tissue in shear: Some experimental data and differential constitutive model. Biorheology, 2001, 38(4): 335-345.
11. Chatelin S, Deck C, Willinger R. An anisotropic viscous hyperelastic constitutive law for brain material finite-element modelin. J Biorheol, 2013, 27(1): 26-37.
12. Giordano C, Kleiven S. Connecting fractional anisotropy from medical images with mechanical anisotropy of a hyperviscoelastic fibre-reinforced constitutive model for brain tissue. J Royal Soc Interf, 2014, 11(91): 20130914.
13. Wright R M, Post A, Hoshizaki B, et al. A multiscale computational approach to estimating axonal damage under inertial loading of the head. J Neurotrauma, 2013, 30(2): 102-118.
14. Weiss J A, Maker B N, Govindjee S. Finite element implementation of incompressible, transversely isotropic hyperelasticity. Comput Methods Appl Mech Engrg, 1996, 135(1/2): 107-128.
15. Puso M A, Weiss J A. Finite element implementation of anisotropic quasi-linear viscoelasticity using a discrete spectrum approximation. J Biomech Eng, 1998, 120(1): 62-70.
16. Fung Y C. Biomechanics: mechanical properties of living tissues. 2nd ed. Springer-Verlag New York, 1993.
17. Rashid B, Destrade M, Gilchrist M D. Mechanical characterization of brain tissue in tension at dynamic strain rates. J Mech Behav Biomed Mater, 2014, 33(SI): 43-54.
18. Poles S. MOGA-II an improved multi-objective genetic algorithm. Trieste: Estecotechnical Techincal Report 6, 2003.
19. Chatelin S, Constantinesco A, Willinger R. Fifty years of brain tissue mechanical testing: From in vitro to in vivo investigations. Biorheology, 2010, 47(5/6): 255-276.
20. Rashid B, Destrade M, Gilchrist M D. Mechanical characterization of brain tissue in compression at dynamic strain rates. J Mech Behav Biomed Mater, 2012, 10(1): 23-38.