Study on Material Parameters Identification of Brain Tissue Considering Uncertainty of Friction Coefficient

3rd Annual International Workshop on Materials Science and Engineering

Guan, Fengjiao^1 ; Zhang, Guanjun^2 ; Liu, Jie^2 ; Wang, Shujing^2 ; Luo, Xu^1 ; Zhu, Feng^3

Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha

410073, China^1

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha

410082, China^2

Department of Mechanical Engineering, Embry-Riddle Aeronautical University, Daytona Beach

FL, United States^3

关键词: Brain tissue parameters;    Friction coefficients;    Intelligent optimization algorithm;    Interval analysis method;    Irregular geometries;    Material parameter;    Uncertain friction coefficients;    Visco-elastic material;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/250/1/012049/pdf
DOI  :  10.1088/1757-899X/250/1/012049

来源: IOP

【 摘 要 】

Accurate material parameters are critical to construct the high biofidelity finite element (FE) models. However, it is hard to obtain the brain tissue parameters accurately because of the effects of irregular geometry and uncertain boundary conditions. Considering the complexity of material test and the uncertainty of friction coefficient, a computational inverse method for viscoelastic material parameters identification of brain tissue is presented based on the interval analysis method. Firstly, the intervals are used to quantify the friction coefficient in the boundary condition. And then the inverse problem of material parameters identification under uncertain friction coefficient is transformed into two types of deterministic inverse problem. Finally the intelligent optimization algorithm is used to solve the two types of deterministic inverse problems quickly and accurately, and the range of material parameters can be easily acquired with no need of a variety of samples. The efficiency and convergence of this method are demonstrated by the material parameters identification of thalamus. The proposed method provides a potential effective tool for building high biofidelity human finite element model in the study of traffic accident injury.