Gaewsky James P, Weaver Ashley A, Koya Bharath, Stitzel Joel D
a Wake Forest University School of Medicine , Winston-Salem , North Carolina.
b Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences , Winston-Salem , North Carolina.
Traffic Inj Prev. 2015;16 Suppl 2:S124-31. doi: 10.1080/15389588.2015.1061666.
A 3-phase real-world motor vehicle crash (MVC) reconstruction method was developed to analyze injury variability as a function of precrash occupant position for 2 full-frontal Crash Injury Research and Engineering Network (CIREN) cases.
Phase I: A finite element (FE) simplified vehicle model (SVM) was developed and tuned to mimic the frontal crash characteristics of the CIREN case vehicle (Camry or Cobalt) using frontal New Car Assessment Program (NCAP) crash test data. Phase II: The Toyota HUman Model for Safety (THUMS) v4.01 was positioned in 120 precrash configurations per case within the SVM. Five occupant positioning variables were varied using a Latin hypercube design of experiments: seat track position, seat back angle, D-ring height, steering column angle, and steering column telescoping position. An additional baseline simulation was performed that aimed to match the precrash occupant position documented in CIREN for each case. Phase III: FE simulations were then performed using kinematic boundary conditions from each vehicle's event data recorder (EDR). HIC15, combined thoracic index (CTI), femur forces, and strain-based injury metrics in the lung and lumbar vertebrae were evaluated to predict injury.
Tuning the SVM to specific vehicle models resulted in close matches between simulated and test injury metric data, allowing the tuned SVM to be used in each case reconstruction with EDR-derived boundary conditions. Simulations with the most rearward seats and reclined seat backs had the greatest HIC15, head injury risk, CTI, and chest injury risk. Calculated injury risks for the head, chest, and femur closely correlated to the CIREN occupant injury patterns. CTI in the Camry case yielded a 54% probability of Abbreviated Injury Scale (AIS) 2+ chest injury in the baseline case simulation and ranged from 34 to 88% (mean = 61%) risk in the least and most dangerous occupant positions. The greater than 50% probability was consistent with the case occupant's AIS 2 hemomediastinum. Stress-based metrics were used to predict injury to the lower leg of the Camry case occupant. The regional-level injury metrics evaluated for the Cobalt case occupant indicated a low risk of injury; however, strain-based injury metrics better predicted pulmonary contusion. Approximately 49% of the Cobalt occupant's left lung was contused, though the baseline simulation predicted 40.5% of the lung to be injured.
A method to compute injury metrics and risks as functions of precrash occupant position was developed and applied to 2 CIREN MVC FE reconstructions. The reconstruction process allows for quantification of the sensitivity and uncertainty of the injury risk predictions based on occupant position to further understand important factors that lead to more severe MVC injuries.
开发一种三相真实世界机动车碰撞(MVC)重建方法,以分析2例全正面碰撞损伤研究与工程网络(CIREN)案例中,碰撞前乘员位置与损伤变异性之间的关系。
第一阶段:开发并调整有限元(FE)简化车辆模型(SVM),利用正面新车评估程序(NCAP)碰撞测试数据,模拟CIREN案例车辆(凯美瑞或科鲁兹)的正面碰撞特性。第二阶段:将丰田安全人体模型(THUMS)v4.01在SVM内针对每个案例设置120种碰撞前配置。使用拉丁超立方实验设计改变五个乘员定位变量:座椅轨道位置、座椅靠背角度、D形环高度、转向柱角度和转向柱伸缩位置。另外进行了一次基线模拟,旨在匹配CIREN记录的每个案例的碰撞前乘员位置。第三阶段:然后使用每辆车事件数据记录器(EDR)的运动边界条件进行有限元模拟。评估头部损伤指标(HIC15)、胸部综合指数(CTI)、股骨受力以及肺和腰椎基于应变的损伤指标,以预测损伤情况。
将SVM调整为特定车辆模型后,模拟损伤指标数据与测试数据紧密匹配,使得调整后的SVM能够用于每个案例,并结合EDR导出的边界条件进行重建。座椅最靠后且座椅靠背倾斜的模拟中,HIC15、头部损伤风险、CTI和胸部损伤风险最大。计算得出的头部、胸部和股骨损伤风险与CIREN乘员损伤模式密切相关。在凯美瑞案例的基线模拟中,CTI显示缩写损伤量表(AIS)2级及以上胸部损伤的概率为54%,在最不危险和最危险的乘员位置,风险范围为34%至88%(平均 = 61%)。超过50%的概率与案例乘员的AIS 2级血胸一致。基于应力的指标用于预测凯美瑞案例乘员小腿的损伤。对科鲁兹案例乘员评估的区域级损伤指标表明损伤风险较低;然而,基于应变的损伤指标能更好地预测肺挫伤。科鲁兹乘员约49%的左肺受到挫伤,尽管基线模拟预测有40.5%的肺会受伤。
开发了一种根据碰撞前乘员位置计算损伤指标和风险的方法,并将其应用于2例CIREN MVC有限元重建。该重建过程能够量化基于乘员位置的损伤风险预测的敏感性和不确定性,以进一步了解导致更严重MVC损伤的重要因素。