Evaluation of geometrically personalized THUMS pedestrian model response against sedan–pedestrian PMHS impact test data

Objective: Evaluating the biofidelity of pedestrian finite element models (PFEM) using postmortem human subjects (PMHS) is a challenge because differences in anthropometry between PMHS and PFEM could limit a model's capability to accurately capture cadaveric responses. Geometrical personalization via morphing can modify the PFEM geometry to match the specific PMHS anthropometry, which could alleviate this issue. In this study, the Total Human Model for Safety (THUMS) PFEM (Ver 4.01) was compared to the cadaveric response in vehicle–pedestrian impacts using geometrically personalized models.

Methods: The AM50 THUMS PFEM was used as the baseline model, and 2 morphed PFEM were created to the anthropometric specifications of 2 obese PMHS used in a previous pedestrian impact study with a mid-size sedan. The same measurements as those obtained during the PMHS tests were calculated from the simulations (kinematics, accelerations, strains), and biofidelity metrics based on signals correlation (correlation and analysis, CORA) were established to compare the response of the models to the experiments. Injury outcomes were predicted deterministically (through strain-based threshold) and probabilistically (with injury risk functions) and compared with the injuries reported in the necropsy.

Results: The baseline model could not accurately capture all aspects of the PMHS kinematics, strain, and injury risks, whereas the morphed models reproduced biofidelic response in terms of trajectory (CORA score = 0.927 ± 0.092), velocities (0.975 ± 0.027), accelerations (0.862 ± 0.072), and strains (0.707 ± 0.143). The personalized THUMS models also generally predicted injuries consistent with those identified during posttest autopsy.

Conclusions: The study highlights the need to control for pedestrian anthropometry when validating pedestrian human body models against PMHS data. The information provided in the current study could be useful for improving model biofidelity for vehicle–pedestrian impact scenarios.     

Driver Injury Risk Variability in Finite Element Reconstructions of Crash Injury Research and Engineering Network (CIREN) Frontal Motor Vehicle Crashes

Objective: 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.

Method: 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.

Results: 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.

Conclusions: 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.     

Sensitivity of Head and Cervical Spine Injury Measures to Impact Factors Relevant to Rollover Crashes

Objective: Serious head and cervical spine injuries have been shown to occur mostly independent of one another in pure rollover crashes. In an attempt to define a dynamic rollover crash test protocol that can replicate serious injuries to the head and cervical spine, it is important to understand the conditions that are likely to produce serious injuries to these 2 body regions. The objective of this research is to analyze the effect that impact factors relevant to a rollover crash have on the injury metrics of the head and cervical spine, with a specific interest in the differentiation between independent injuries and those that are predicted to occur concomitantly.

Methods: A series of head impacts was simulated using a detailed finite element model of the human body, the Total HUman Model for Safety (THUMS), in which the impactor velocity, displacement, and direction were varied. The performance of the model was assessed against available experimental tests performed under comparable conditions. Indirect, kinematic-based, and direct, tissue-level, injury metrics were used to assess the likelihood of serious injuries to the head and cervical spine.

Results: The performance of the THUMS head and spine in reconstructed experimental impacts compared well to reported values. All impact factors were significantly associated with injury measures for both the head and cervical spine. Increases in impact velocity and displacement resulted in increases in nearly all injury measures, whereas impactor orientation had opposite effects on brain and cervical spine injury metrics. The greatest cervical spine injury measures were recorded in an impact with a 15° anterior orientation. The greatest brain injury measures occurred when the impactor was at its maximum (45°) angle.

Conclusions: The overall kinetic and kinematic response of the THUMS head and cervical spine in reconstructed experiment conditions compare well with reported values, although the occurrence of fractures was overpredicted. The trends in predicted head and cervical spine injury measures were analyzed for 90 simulated impact conditions. Impactor orientation was the only factor that could potentially explain the isolated nature of serious head and spine injuries under rollover crash conditions. The opposing trends of injury measures for the brain and cervical spine indicate that it is unlikely to reproduce the injuries simultaneously in a dynamic rollover test.