持续性高过载下人脑的多孔弹性响应
Poroelastic Responses of Human Brain Under Sustained High Overloads
田金 1刘少宝 2卢天健 2徐峰3
作者信息
- 1. 西安交通大学第二附属医院,西安 710061;西安交通大学 生命科学与技术学院 生物医学信息工程教育部重点实验室,西安 710049;西安交通大学 仿生工程与生物力学研究所,西安 710049
- 2. 南京航空航天大学 航空航天结构力学及控制全国重点实验室,南京 210016;南京航空航天大学 多功能轻量化材料与结构工信部重点实验室,南京 210016
- 3. 西安交通大学 生命科学与技术学院 生物医学信息工程教育部重点实验室,西安 710049;西安交通大学 仿生工程与生物力学研究所,西安 710049
- 折叠
摘要
航空航天飞行期间经常出现的持续性高过载,对乘员的脑功能有重要影响,而脑功能受脑组织力学行为的影响,且后者与载荷特点高度相关.为预测持续性高过载下的人脑力学响应,该文采用多孔弹性本构描述了脑组织的力学行为,基于简化的头部一维多层结构模型,推导了脑组织的多孔弹性控制方程、状态量传递矩阵,利用La-place变换及其逆变换,得到了颅内液体压力、颅内液体渗流速度、脑组织有效应力、脑组织位移的时空分布.结果表明,颅内液体渗流对脑组织在持续性高过荷下的响应有显著影响.该文强调采用多孔弹性本构描述脑组织力学行为的适切性和必要性,为极端载荷条件下人脑生物力学响应研究提供了重要的理论见解.
Abstract
Sustained high overloads often acting during aerospace flights can significantly affect the passenger brain function dependent on the mechanical behavior of brain tissue and highly correlated with load characteris-tics.To predict the mechanical responses of human brain under sustained high overloads,the poroelastic con-stitutive model was adopted to characterize the mechanical behaviors of brain tissue.Built on an idealized 1D multi-layer structural model for human heads,the poroelastic control equation and the state transfer matrix for the brain tissue were derived.Through the Laplace transform and its inverse transform,the spatiotemporal dis-tribution of the intracranial fluid pressure,the intracranial fluid seepage velocity,the brain tissue effective stress,and the brain tissue displacement were obtained.The results indicate that,the intracranial fluid infiltra-tion has a significant impact on the responses of the brain tissue under sustained high overloads.The present work emphasizes the appropriateness and necessity of using poroelastic constitutive models to describe the me-chanical behavior of brain tissue,providing important theoretical insights for the study of brain biomechanics under extreme load conditions.
关键词
脑组织力学响应/持续性高过载/多孔弹性本构Key words
brain tissue mechanical response/sustained high overload/poroelastic constitutive model引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据