Cardiovascular Engineering Research Lab (CERL), School of Mechanical and Design Engineering, University of Portsmouth, Anglesea Road, Portsmouth, PO1 3DJ, United Kingdom.
Dipartimento di Ingegneria, Universita degli studi di Perugia, Via G. Duranti, Perugia, 06125, Italy.
J Mech Behav Biomed Mater. 2024 May;153:106502. doi: 10.1016/j.jmbbm.2024.106502. Epub 2024 Mar 10.
A new modelling approach is employed in this work for application to the rate-dependent mechanical behaviour of the brain tissue, as an incompressible isotropic material. Extant datasets encompassing single- and multi-mode compression, tension and simple shear deformation(s) are considered, across a wide range of deformation rates from quasi-static to rates akin to blast loading conditions, in the order of 1000 s . With a simple functional form and a reduced number of parameters, the model is shown to capture the considered rate-dependent behaviours favourably, including in both single- and multi-mode deformation fits, and over all range of deformation rates. The provided modelling results here are obtained from either first fitting the model to the quasi-static data, or/and predicting the behaviour at a different rate than those used for calibrating the model parameters. Given its simplicity, versatility, predictive capability and accuracy, the application of the utilised modelling framework in this work to the rate-dependent mechanical behaviour of the brain tissue is proposed.
本工作采用了一种新的建模方法，应用于作为不可压缩各向同性材料的脑组织的率相关力学行为。考虑了涵盖单模和多模压缩、拉伸和简单剪切变形的现有数据集，变形率范围从准静态到类似于爆炸加载条件的速率，高达 1000 s。该模型具有简单的函数形式和较少的参数，能够很好地捕捉所考虑的率相关行为，包括在单模和多模变形拟合中，以及在所有变形率范围内。这里提供的建模结果是通过首先将模型拟合到准静态数据，或者/和预测与用于校准模型参数的速率不同的速率下的行为来获得的。鉴于其简单性、多功能性、预测能力和准确性，本工作提出将所使用的建模框架应用于脑组织的率相关力学行为。
