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电动二轮车驾驶人头部损伤再现不确定性方法
引用本文:韩勇,徐甲芍,石亮亮,高秀晶,钱宇彬,杨震.电动二轮车驾驶人头部损伤再现不确定性方法[J].中国公路学报,2020,33(1):172-180,190.
作者姓名:韩勇  徐甲芍  石亮亮  高秀晶  钱宇彬  杨震
作者单位:1. 厦门理工学院机械与汽车工程学院, 福建厦门 361024;2. 厦门理工学院福建省客车及特种车辆研发 协同创新中心, 福建厦门 361024;3. 厦门大学航空航天学院, 福建厦门 361005;4. 上海工程技术大学 汽车工程学院, 上海 201620;5. 浙江省汽车安全技术研究重点实验室, 浙江宁波 315336
基金项目:国家自然科学基金项目(51775466,51675454);国家外专局高端团队项目(GDT20173600037);福建省科技创新平台项目(2016H2003);浙江省汽车安全技术研究重点实验室开放基金项目(LHY1806Y00285)
摘    要:为了减少交通事故中不确定性信息在受伤害者损伤再现中造成的不利影响,采用拉丁超立方(LHS)试验设计和响应面蒙特卡洛相结合的方法对电动二轮车驾驶人头部损伤再现进行损伤不确定性分析。首先采用多体系统动力学方法对具有详细事故信息(含视频信息)和损伤记录的2起电动二轮车事故中的汽车碰撞速度进行再现和不确定性分析,并对比事故信息(视频信息、最终位置、电动二轮车驾驶人的运动学),进而验证事故再现结果的有效性。在此基础上,应用有限元方法将获得的电动二轮车驾驶人头部碰撞的边界条件加载至THUMS人体头部有限元模型,分析电动二轮车驾驶人头部损伤参数的不确定性与简明损伤准则AIS累计频率的分布关系,并对比电动二轮车事故案例中电动二轮车驾驶人的头部损伤法医鉴定记录。研究结果表明:蒙特卡洛不确定性分析方法能够较准确地预测电动二轮车事故中的汽车碰撞车速,采用该分析方法获得的电动二轮车驾驶人头部损伤等级与法医鉴定的脑部损伤记录高度吻合;蒙特卡洛不确定性分析方法可以适用于评估电动二轮车事故中电动二轮车驾驶人的头部损伤等级,研究结果可为电动二轮车驾驶人头部损伤研究提供理论依据和实证方法。

关 键 词:汽车工程  电动二轮车事故  不确定性分析  头部损伤  试验设计  蒙特卡洛方法
收稿时间:2018-11-23

Uncertainty Analysis of Head Injury via Reconstruction of Electric Two-wheeler Accidents
HAN Yong,XU Jia-shao,SHI Liang-liang,GAO Xiu-jing,QIAN Yu-bin,YANG Zhen.Uncertainty Analysis of Head Injury via Reconstruction of Electric Two-wheeler Accidents[J].China Journal of Highway and Transport,2020,33(1):172-180,190.
Authors:HAN Yong  XU Jia-shao  SHI Liang-liang  GAO Xiu-jing  QIAN Yu-bin  YANG Zhen
Institution:(Mechanical and Automotive Engineering,Xiamen University of Technology,Xiamen 361024,Fujian,China;Fujian Collaborative Innovation Center for R&D of Coach and Special Vehicle,Xiamen University of Technology,Xiamen 361024,Fujian,China;School of Aerospace Engineering,Xiamen University,Xiamen 361005,Fujian,China;School of Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Key Laboratory of Automobile Safety Technology of Zhejiang Province,Ningbo 315336,Zhejiang,China)
Abstract:Uncertain information has a negative influence on the accuracy of injury reconstruction of vulnerability in an accident. This study combined Latin hypercube sampling (LHS) design with a response surface Monte Carlo method to address head injury uncertainties. Two electric two-wheeler (ETW) accidents were reconstructed using a multi-body system, in which accident collision information (video information, final position, and kinematics of riders) was compared with simulation results to verify the effectiveness of the accident reconstruction. The head impact conditions of the riders were used as the boundary conditions in each injury reconstruction by using finite element method. Then, the total human model for safety (THUMS) finite element model of the pedestrian head was adopted in the analysis. The parameters of the riders' head injuries were analyzed, and their distributions on the cumulative frequency abbreviated injury scale (AIS), as predicted by the simulations, were compared with the head injury forensic identification records. Results show that Monte Carlo uncertainty analysis can accurately predict vehicle collision velocity in ETW accidents. In addition, the head injury levels obtained by uncertainty analysis are highly consistent with the forensic brain injury records. The research clearly indicates that Monte Carlo uncertainty analysis can be used to predict the level of a rider's head injury and can provide a theoretical basis and an empirical approach to investigate the head injuries of riders in ETW accidents.
Keywords:automotive engineering  electric two-wheeler accidents  uncertainty analysis  head injuries  experimental design  Monte Carlo methods  
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