Suppr超能文献

将可变形脊柱和 3D 颈部肌肉组织模块化并入简化人体有限元模型。

Modular incorporation of deformable spine and 3D neck musculature into a simplified human body finite element model.

机构信息

Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Virginia Tech-Wake Forest University Center for Injury Biomechanics, Winston-Salem, NC, USA.

出版信息

Comput Methods Biomech Biomed Engin. 2024 Jan-Mar;27(1):45-55. doi: 10.1080/10255842.2023.2168537. Epub 2023 Jan 19.

Abstract

Spinal injuries are a concern for automotive applications, requiring large parametric studies to understand spinal injury mechanisms under complex loading conditions. Finite element computational human body models (e.g. Global Human Body Models Consortium (GHBMC) models) can be used to identify spinal injury mechanisms. However, the existing GHBMC detailed models (with high computational time) or GHBMC simplified models (lacking vertebral fracture prediction capabilities) are not ideal for studying spinal injury mechanisms in large parametric studies. To overcome these limitations, a modular 50 percentile male simplified occupant model combining advantages of both the simplified and detailed models, M50-OS + DeformSpine, was developed by incorporating the deformable spine and 3D neck musculature from the detailed GHBMC model M50-O (v6.0) into the simplified GHBMC model M50-OS (v2.3). This new modular model was validated against post-mortem human subject test data in four rigid hub impactor tests and two frontal impact sled tests. The M50-OS + DeformSpine model showed good agreement with experimental test data with an average CORrelation and Analysis (CORA) score of 0.82 for the hub impact tests and 0.75 for the sled impact tests. CORA scores were statistically similar overall between the M50-OS + DeformSpine (0.79 ± 0.11), M50-OS (0.79 ± 0.11), and M50-O (0.82 ± 0.11) models ( > 0.05). This new model is computationally 6 times faster than the detailed M50-O model, with added spinal injury prediction capabilities over the simplified M50-OS model.

摘要

脊柱损伤是汽车应用的关注点,需要进行大量参数研究才能了解复杂加载条件下的脊柱损伤机制。有限元计算人体模型(例如全球人体模型联盟(GHBMC)模型)可用于确定脊柱损伤机制。然而,现有的 GHBMC 详细模型(计算时间长)或 GHBMC 简化模型(缺乏预测椎体骨折的能力)并不适合在大型参数研究中研究脊柱损伤机制。为了克服这些限制,通过将详细 GHBMC 模型 M50-O(v6.0)中的可变形脊柱和 3D 颈部肌肉组织纳入简化 GHBMC 模型 M50-OS(v2.3),开发了一种模块化 50 百分位男性简化乘员模型 M50-OS + DeformSpine,它结合了简化模型和详细模型的优势。该新的模块化模型在四项刚性中心冲击器测试和两项正面碰撞滑橇测试中通过与尸体人体测试数据进行了验证。M50-OS + DeformSpine 模型与实验测试数据吻合良好,中心冲击器测试的平均 CORrelation and Analysis(CORA)评分为 0.82,滑橇冲击测试的平均 CORA 评分为 0.75。M50-OS + DeformSpine(0.79±0.11)、M50-OS(0.79±0.11)和 M50-O(0.82±0.11)模型之间的 CORA 评分总体上无统计学差异(>0.05)。与详细的 M50-O 模型相比,该新模型的计算速度快 6 倍,并且在简化的 M50-OS 模型基础上增加了脊柱损伤预测能力。

相似文献

1
Modular incorporation of deformable spine and 3D neck musculature into a simplified human body finite element model.
Comput Methods Biomech Biomed Engin. 2024 Jan-Mar;27(1):45-55. doi: 10.1080/10255842.2023.2168537. Epub 2023 Jan 19.
2
Development of a computationally efficient full human body finite element model.
Traffic Inj Prev. 2015;16 Suppl 1:S49-56. doi: 10.1080/15389588.2015.1021418.
3
Modular use of human body models of varying levels of complexity: Validation of head kinematics.
Traffic Inj Prev. 2017 May 29;18(sup1):S155-S160. doi: 10.1080/15389588.2017.1315637.
5
Biofidelity assessment of the GHBMC M50-O in a rear-facing seat configuration during high-speed frontal impact.
Comput Methods Biomech Biomed Engin. 2024 Aug;27(10):1287-1302. doi: 10.1080/10255842.2023.2239417. Epub 2023 Sep 8.
6
Development and Validation of an Active Muscle Simplified Finite Element Human Body Model in a Standing Posture.
Ann Biomed Eng. 2023 Mar;51(3):632-641. doi: 10.1007/s10439-022-03077-x. Epub 2022 Sep 20.
8
Development and preliminary validation of computationally efficient and detailed 50th percentile female human body models.
Accid Anal Prev. 2023 Sep;190:107182. doi: 10.1016/j.aap.2023.107182. Epub 2023 Jun 28.
9
Validation of a simplified human body model in relaxed and braced conditions in low-speed frontal sled tests.
Traffic Inj Prev. 2019;20(8):832-837. doi: 10.1080/15389588.2019.1655733. Epub 2019 Sep 24.
10
Implementation and calibration of active small female and average male human body models using low-speed frontal sled tests.
Traffic Inj Prev. 2022;23(sup1):S44-S49. doi: 10.1080/15389588.2022.2114078. Epub 2022 Sep 15.

本文引用的文献

1
Occupant Injury and Response on Oblique-Facing Aircraft Seats: A Computational Study.
J Biomech Eng. 2023 Feb 1;145(2). doi: 10.1115/1.4055511.
2
Sensitivity Analysis for Multidirectional Spaceflight Loading and Muscle Deconditioning on Astronaut Response.
Ann Biomed Eng. 2023 Feb;51(2):430-442. doi: 10.1007/s10439-022-03054-4. Epub 2022 Aug 26.
4
Effect of various restraint configurations on submarining occurrence across varied seat configurations in autonomous driving system environment.
Traffic Inj Prev. 2021;22(sup1):S128-S133. doi: 10.1080/15389588.2021.1939872. Epub 2021 Aug 17.
5
Vestibulocollic and Cervicocollic Muscle Reflexes in a Finite Element Neck Model During Multidirectional Impacts.
Ann Biomed Eng. 2021 Jul;49(7):1645-1656. doi: 10.1007/s10439-021-02783-2. Epub 2021 May 3.
6
Kinematic and Injury Response of Reclined PMHS in Frontal Impacts.
Stapp Car Crash J. 2020 Nov;64:83-153. doi: 10.4271/2020-22-0004.
7
Brain response of a computational head model for prescribed skull kinematics and simulated football helmet impact boundary conditions.
J Mech Behav Biomed Mater. 2021 Mar;115:104299. doi: 10.1016/j.jmbbm.2020.104299. Epub 2021 Jan 5.
8
Thoracolumbar spine kinematics and injuries in frontal impacts with reclined occupants.
Traffic Inj Prev. 2020 Oct 12;21(sup1):S66-S71. doi: 10.1080/15389588.2020.1837365. Epub 2020 Nov 18.
9
Simulation-based assessment of injury risk for an average male motorsport driver.
Traffic Inj Prev. 2020 Oct 12;21(sup1):S72-S77. doi: 10.1080/15389588.2020.1802021. Epub 2020 Aug 28.
10
Submarining sensitivity across varied seat configurations in autonomous driving system environment.
Traffic Inj Prev. 2020 Oct 12;21(sup1):S1-S6. doi: 10.1080/15389588.2020.1791324. Epub 2020 Jul 13.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验