Brain's strain-rate-enhancement characteristic and a strong nonlinear viscoelastic model

IF 9.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL International Journal of Mechanical Sciences Pub Date : 2025-02-15 Epub Date: 2025-01-23 DOI:10.1016/j.ijmecsci.2025.110003
Jingyu Wang , Zexuan Chen , Taolin Sun , Zhenyu Jiang , Licheng Zhou , Zejia Liu , Yiping Liu , Bao Yang , Liqun Tang
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Abstract

It is widely recognized that brain tissue exhibits a significant strain rate effect. However, due to technical limitations, the mechanical behavior of brain tissue within the strain rate range of 100–500 s⁻¹ remains poorly understood, leaving the accuracy of existing constitutive models for brain tissue inadequately validated. In this study, we employed a Long Split Hopkinson Pressure Bar (LSHPB) system designed for ultra-soft materials to characterize the mechanical behavior of brain tissue at strain rates of 125 s⁻¹ and 340 s⁻¹, thereby addressing the research gap concerning brain tissue behavior under intermediate strain rates. By integrating experimental data from low and high strain rate tests (0.001 s⁻¹, 0.1 s⁻¹, 700 s⁻¹, 900 s⁻¹, and 1700 s⁻¹, respectively), we further observed a significant shift in the strain rate enhancement effect within the intermediate strain rate range. This suggests that current rate-dependent constitutive models are insufficient to accurately describe the comprehensive rate-dependent mechanical behavior of brain tissue. Consequently, we developed a highly nonlinear viscoelastic model capable of effectively describing the mechanical behavior of brain tissue across low, intermediate, and high strain rate ranges. Our work accurately characterizes the large deformation behavior of brain tissue under intermediate strain rates for the first time, revealing its strong nonlinear strain rate enhancement characteristics. Additionally, a suitable constitutive model is proposed. This study not only provides comprehensive insights into the rate-dependent mechanical behaviors of brain tissue but also holds great potential for improving the accuracy of Finite Element Head Modeling (FEHM).

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脑应变率增强特性及强非线性粘弹性模型
人们普遍认为脑组织表现出显著的应变率效应。然而,由于技术上的限制,脑组织在100-500秒应变率范围内的力学行为仍然知之甚少,使得现有的脑组织本构模型的准确性没有得到充分的验证。在这项研究中,我们采用了专为超软材料设计的长裂霍普金森压力棒(LSHPB)系统来表征125 s⁻¹和340 s⁻¹应变速率下脑组织的力学行为,从而解决了在中等应变速率下脑组织行为的研究空白。通过整合低应变率和高应变率测试的实验数据(分别为0.001 s⁻¹,0.1 s⁻¹,700 s⁻¹,900 s⁻¹和1700 s⁻¹),我们进一步观察到在中等应变率范围内应变率增强效果的显著变化。这表明当前速率相关的本构模型不足以准确描述脑组织的综合速率相关力学行为。因此,我们开发了一种高度非线性的粘弹性模型,能够有效地描述脑组织在低、中、高应变率范围内的力学行为。本研究首次准确表征了脑组织在中等应变率下的大变形行为,揭示了其较强的非线性应变率增强特性。此外,还提出了合适的本构模型。该研究不仅对脑组织的速率相关力学行为提供了全面的见解,而且对提高头部有限元建模(FEHM)的准确性具有很大的潜力。
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来源期刊
International Journal of Mechanical Sciences
International Journal of Mechanical Sciences 工程技术-工程:机械
CiteScore
12.80
自引率
17.80%
发文量
769
审稿时长
19 days
期刊介绍: The International Journal of Mechanical Sciences (IJMS) serves as a global platform for the publication and dissemination of original research that contributes to a deeper scientific understanding of the fundamental disciplines within mechanical, civil, and material engineering. The primary focus of IJMS is to showcase innovative and ground-breaking work that utilizes analytical and computational modeling techniques, such as Finite Element Method (FEM), Boundary Element Method (BEM), and mesh-free methods, among others. These modeling methods are applied to diverse fields including rigid-body mechanics (e.g., dynamics, vibration, stability), structural mechanics, metal forming, advanced materials (e.g., metals, composites, cellular, smart) behavior and applications, impact mechanics, strain localization, and other nonlinear effects (e.g., large deflections, plasticity, fracture). Additionally, IJMS covers the realms of fluid mechanics (both external and internal flows), tribology, thermodynamics, and materials processing. These subjects collectively form the core of the journal's content. In summary, IJMS provides a prestigious platform for researchers to present their original contributions, shedding light on analytical and computational modeling methods in various areas of mechanical engineering, as well as exploring the behavior and application of advanced materials, fluid mechanics, thermodynamics, and materials processing.
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