Validated numerical unrestrained occupant-seat crash scenarios for high-speed trains integrating experimental, computational, and inverse methods.

OBJECTIVE: Occupant impact safety is critical for train development. This paper proposes a systematic procedure for developing validated numerical occupant crash scenarios for high-speed trains by integrating experimental, computational, and inverse methods. METHODS: As the train interior is the most potentially injury-causing factor, the material properties were acquired by mechanical tests, and constitutive models were calibrated using inverse methods. The validity of the seat material constitutive model was further verified via drop tower tests. Finite element (FE) and multibody (MB) models of train occupant-seat interactions in frontal impact were established in LS-DYNA and MADYMO software, respectively, using the experimentally acquired materials/mechanical characteristics. Three dummy sled crash tests with different folding table and backrest configurations were conducted to validate the numerical occupant-seat models and to further assess occupant injury in train collisions. The occupant impact responses between dummy tests and simulations were quantitatively compared using a correlation and analysis (CORA) objective rating method. RESULTS: Results indicated that the experimentally calibrated numerical seat-occupant models could effectively reproduce the occupant responses in bullet train collisions (CORA scores >80%). Compared with the train seat-occupant MB model, the FE model could simulate the head acceleration with slightly more acceptable fidelity, however, the FE model CORA scores were slightly less than for the MB models. The maximum head acceleration was 30 g but the maximum HIC score was 17.4. When opening the folding table, the occupant's chest injury was not obvious, but the neck-table contact and "chokehold" may potentially be severe and require further assessment. CONCLUSIONS: This study demonstrates the value of experimental data for occupant-seat model interactions in train collisions and provides practical help for train interior safety design and formulation of standards for rolling stock interior passive safety.

Assessment of Pedestrians' Head and Lower Limb Injuries in Tram-Pedestrian Collisions.

Analysis of pedestrians' head and lower limb injuries at the tissue level is lacking in studies of tram-pedestrian collisions. The purpose of this paper therefore to investigate the impact response process and severity of pedestrians' injuries in tram-pedestrian collisions, using the Total Human Model for Safety (THUMS) pedestrian human body model together with the tram FE model. Two full-scale tram-pedestrian dummy crash tests were performed to validate the FE model, and the total correlation and analysis (CORA) score of head acceleration yielded values of 0.840 and 0.734, confirming a strong agreement between the FE-simulated head responses and the experimental head kinematics. The effects of different tram speeds and impact angles on pedestrians' impact response injuries and the differences were further analyzed. The results indicate that direct impact of the lower limb with the tram's obstacle deflector leads to lower limb bone shaft fractures and knee tissue damage. Neck fling contributed to worsened head injury. Coup contusions were the predominant type of brain contusion, surpassing contrecoup contusions, while diffuse axonal injury was mainly concentrated in the collision-side region of the brain. Pedestrians' injuries are influenced by tram velocity and impact angle: higher tram velocities increase the risk of lower limb and head injuries. The risk of head injury for pedestrians is higher when the impact angle is negative, while lower limb injuries are more significant when the impact angle is 0°. This study provides practical guidance for enhancing tram safety and protecting pedestrians.

An intelligent method for accident reconstruction involving car and e-bike coupling automatic simulation and multi-objective optimizations.

Car-electric bicycle (e-bike) accidents have been the subject of strong attention due to the widespread usage of e-bikes and a high casualty rate for their riders. Manually conducted accident reconstruction is based on the trial-and-error method with a limited number of parameter combinations, which makes it time-consuming and subjective. This paper aims to develop an intelligent method for accurate, high-efficient reconstruction of accidents involving cars and e-bikes. First, an automatic operation framework, which can drive the MADYMO program and perform results analysis automatically, was built with four multi-objective optimization algorithms available - NSGA-Ⅱ, NCGA, AMGA, and MOPS; The optimization condition was controlled with 12 design variables, 5 objective functions, and 3 constraints. Then, a real e-bike accident with surveillance video was reconstructed through the proposed framework to verify its validity using comparisons of simulated and actual rest positions, initial variables, kinematic response, and head injury. Lastly, the simulation data were used to study the effects of the initial variables on objectives with a multiple linear regression model. The results showed that it took only about 24 h in total for optimization with 480 automatic operations. Optimal conditions were searched at run numbers of 469, 430, 323, and 474 for NSGA-Ⅱ, NCGA, AMGA, and MOPS, respectively. NSGA-Ⅱ had the best performance for e-bike accident reconstruction with average errors of objectives below 5%; Good consistencies for the rider's kinematic response in three stages after collision were observed between simulations and screenshots from the surveillance video, as well as for velocities between the simulation and those estimated from the surveillance video and for head injury between the simulation and the medical report. In contrast to the subjective trial-and-error method that highly depends on the analyst's intuition and experience, this intelligent method is based on multi-objective optimization theory, with which results can be optimized in terms of the automatic change of initial variables. All the above comparisons demonstrate that the method is valid for effectively improving efficiency without simultaneously compromising accuracy. This intelligent method, coupling automatic simulation and multi-objective optimization, can also be applied to other accident reconstructions, and the significant order of initial variables' effects on objectives can provide recommendations for further reconstructions.

Assessment of standing passenger traumatic brain injury caused by ground impact in subway collisions.

Human head is the most vulnerable region in subway collisions. To design a safer subway, the head impact biomechanical response should be studied first. This paper aims to investigate the standing passenger head-ground impact dynamic response and traumatic brain injury (TBI) in subway collisions. A standing passenger-subway interior dynamic model was numerically developed by using our previous validated finite element (FE)-multibody (MB) coupled human body model, which was integrated by the Total Human Model for Safety (THUMS) head-neck FE model and the extracted remaining body segments pedestrian MB model of TNO. A parametric study considering the handrail type, standing angle, and friction coefficient between the shoes and ground was performed. Results show that the passenger dynamic response could be divided into two categories according to whether the passenger hit handrails. Passenger TBIs severity could be efficiently alleviated by the passenger body (excluding the head) hitting the handrail first before head-ground impact. The probabilities of DAI in the cerebellum and brain stem were low. A statistical analysis of TBIs demonstrated that the risks of TBIs were sensitive to the handrail type in subway collisions, but did not to the standing angle and friction coefficient. This study provides practical help for improving the interior crashworthiness performance of subways.