This research investigates the challenge of accurately predicting the crashworthiness of tubular nested (TNS) crash-box subjected to both axial and oblique loading conditions. Conventional theoretical approaches frequently exhibit limited generalizability under complex loading scenarios, while data-driven models often lack physical interpretability. This study proposes a comprehensive multi-method framework that synthesizes theoretical modeling, finite element (FE) analysis, and interpretable machine learning. A high-fidelity finite element model, validated against experimental data, is developed to characterize the crushing behavior of TNS and to identify the predominant geometric parameters influencing crashworthiness under various loading conditions. Drawing upon deformation modes identified through experimental observations and finite element analyses, an analytical model for axial crushing is initially formulated utilizing the simplified super-folding element (SSFE) theory. This model is subsequently expanded to accommodate oblique loading conditions. However, the theoretical model shows reduced accuracy under asymmetric deformation conditions. To address this limitation, machine learning models are introduced to improve prediction accuracy. An explicit formulation strategy is further developed to convert the learned relationships into interpretable analytical expressions, addressing the black-box nature of data-driven methods. Results demonstrate that the proposed framework significantly improves prediction accuracy under complex loading while maintaining physical interpretability. This approach provides a scalable solution for crashworthiness prediction and bridges the gap between physics-based and data-driven methods.

The honeycomb-filled structures have better energy absorption performances but cross-effected by the complex interactions between filled honeycomb and metallic tube. Traditional surrogate model-based optimization methods can only ensure that the crashworthiness indicators meet the design expectations. However, unstable buckling and drastic load fluctuations cannot be avoided. In view of this, a multi-dimensional crashworthiness performance prediction and constrained optimization of a novel kind of honeycomb-filled composite structure are investigated. By utilizing the deep learning technique, the deformation images, crashworthiness indicators and load curves of the structure are predicted and introduced as constraints to the multi-objective optimization. Compared to the regular optimization results, the range of the Pareto front is significantly reduced after the introduction of extra constraints. Furthermore, the best solution obtained from the constrained optimization not only satisfy the conventional indicators constraint, but also performs well in terms of the deformation mode and load history. By applying the proposed method, the reliability of the optimization is dramatically improved. It is well proved that the proposed methodology can provide a feasible reference for similar problems of crashworthiness optimization of energy-absorbing structures.

Deep learning is attracting increasing attention due to its excellent predictive power and its ability to be applied in traditional research areas. In this paper, we propose a multisource response prediction network architecture based on a long short-term memory (LSTM)-stacked autoencoder to predict key crashworthiness indicators and complete curve reconstruction. Taking an expansion tube as an example using an equivalent scaling research method, a scaled expandable tubular (SET) finite element model was established and verified using a quasistatic compression test and a full-size coupler and buffer system experiment. A design of experiments (DOE) approach was used to obtain a dataset for training the prediction network. Neural network hyperparameters are critical to network prediction accuracy, and after comparison, the multisource response prediction network architecture showed good computational efficiency and satisfactory prediction accuracy when appropriate hyperparameters were selected. Subsequently, multiobjective constraint optimization was performed using the nondominated sorting genetic algorithm-II (NSGA-II) based on a prediction network architecture, which greatly improved the energy-absorption structure optimization accuracy. The results are expected to provide a research methodology for solving complex engineering problems by establishing a new framework for deep learning algorithms combined with optimization methods.

The habit of using mobile phones with head down and forward leads more and more forward leaning postures in urban rail trains. The impact of the forward leaning posture on collision injury of passengers is worthy of attention. The injury mechanism of the passenger in forward leaning posture is obtained by sled impact test. The passenger injury of 20 groups in different postures is obtained by numerical simulation, and the influence of the posture on passenger injury is analyzed by COPRAS method. The results shown that with changes in the torso angle α and neck angle β, the dynamic response of the passenger in the forward leaning posture can be divided into four types. Among them, the overall injury of passenger in type 4 whose heads did not collide with the seats was lower. The quantitative utility (Ui) of the forward leaning posture with the least overall injury (α = 85° and β = 16°) is only 34.07

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.

Artificial neural networks have drawn growing attention for their outstanding predictive capability combined with traditional research methods. This paper aims to propose a vehicle running attitude prediction model based on Artificial Neural Network-Parallel Connected (ANN-PL), predicting the longitudinal displacement (Svx) and vertical displacement (Svz) of the vehicle body, the vehicle head-up angle (α), and the overriding risk (Cd). The 3D multibody dynamics model (MBD) of the single-vehicle impact on the rigid wall, namely 3D-MBD-SV, was established and validated by the experimental full-scale vehicle collision test. Based on the reliable 3D-MBD-SV, the design of experiment (DOE) approach was carried out to obtain the datasets for training the ANN-PL. The ANN-PL exhibited excellent computational efficiency and satisfactory prediction accuracy compared to the multibody dynamics and finite element simulation calculation methods. However, the different network hyperparameters of the ANN-PL network are essential to prediction accuracy, considering the number of hidden layers and neurons in this paper. In terms of the variables factor analysis, the change of Mean Square Error (MSE) method (COM) in the ANN-PL was used to explore the relationship between the eleven essential input variables and vehicle running attitude. It was found that the maximum relative contribution in ANN-PL (Svx, Svz, α, Cd) is vehicle body mass (Mc) at 70.65%, impact velocity (Vx) at 43.39%, vertical offset of the vehicle body center mass (CMz) at 30.14%, and primary suspension axle box spring vertical travel (Dpz) at 13.63%, respectively. The outcome of this study is expected to provide a research method to solve the complicated engineering issue by building a new artificial neural network algorithmic framework combined with the multibody dynamics and finite element simulation calculation methods.

The contact between passenger and the vehicle interior especially sidewall in train overturn derailment accident cause extremely serious injuries to passenger. It is regrettable that there are few relevant studies that account for overturning accidents and passenger injuries. In this study, the impact responses subject to sidewall and passenger injuries during train-overturn derailments are investigated. First, a theoretical model and a practical accident are used to validate the overturning multi-body dynamic model of an 8-car Electric Multiple Unit (EMU) coupled with a passenger. And then, the passenger's kinematic responses of baseline models with tray tables put back and put down are studied. The results of simulation cases with different train speeds (V), friction coefficients, chair distances (D), and passenger positions (P) show that V and D have a positive impact on passenger injuries. While the friction coefficient has an overall negative influence on the passenger's injuries, there are some irregular fluctuations. Generally, the sitting position farther away from the sidewall tends to be more dangerous for passengers. And the tray table plays an important role in protecting passengers under the same conditions. This study provides referential value for the study of train overturning accidents and guidance for the protection of passengers.

A reliable critical-scenario-based safety assessment of autonomous vehicles in China requires a thorough understanding of complex crash scenarios in Chinese background traffic. Based on actual crashes between a vehicle and a powered two-wheeler (PTW) in China, this study generated the autonomous driving testing scenarios from functional, logical and concrete levels. First, 239 video-recorded crash cases were selected from the China In-depth mobility Safety Study - Traffic Accident (CIMSS-TA) database. Using the k-medoids clustering method, six functional scenarios were generalized according to seven crash characteristics (time of day, road type, road surface, obstruction, motion of vehicle, motion of PTW, relative moving direction and position of PTW with respect to vehicle), which contained two straight road scenarios, two T-junction scenarios and two intersection scenarios. Then, using a trajectory analysis program written by Python, the dangerous time instant of each crash was extracted based on the relative trajectory. According to five dynamic parameters of dangerous time instant, namely vehicle velocity (Vehicle_V), PTW X'-coordinate velocity (PTW_VX'), PTW Y'-coordinate velocity (PTW_VY'), PTW X'-coordinate relative position (PTW_LocX') and PTW Y'-coordinate relative position (PTW_LocY'), a crash trigger scheme was built to remain a case challenging when the involved vehicle is replaced by an autonomous vehicle with completely different maneuvers. Using the kernel density estimation (KDE), the logical scenarios were evolved by calculating the distribution of these dynamic parameters in each cluster. The results showed that there were differences in the distribution of dynamic parameters between six functional scenarios. For instance, the Vehicle_V in the scenario where a vehicle turning right impacts with a right/right rear PTW traveling straight ahead was higher than that in the scenario where a vehicle changing to the left lane impacts with a left/left rear PTW traveling straight ahead, with ranges of (10 km/h, 30 km/h) and (5 km/h, 15 km/h), respectively. Finally, considering the correlation of dynamic parameters, a virtual crash generation approach based on the independent component analysis (ICA) representing the original crashes with independent parameters was proposed to obtain sufficient concrete testing scenarios. The results showed that the statistical characteristics of virtual crashes were consistent with those of original crashes. Therefore, the virtual crash generation approach was effective. And a concrete crossing testing scenario with the crash trigger conditions of Vehicle_V = 26.272 km/h, PTW_VX' = 15.567 km/h, PTW_VY' = -1.670 km/h, PTW_LocX' = -27.265 m and PTW_LocY' = 52. 149 m was especially demonstrated. This study provides a theoretical basis for generating autonomous driving testing scenarios and data support for establishing relevant testing schemes tailored to the traffic environment in China.

An auxiliary protection device (rail holding mechanism) was proposed to control the collision attitude of subway vehicles. The dynamics model of head-on collision of subway vehicles was established and verified by the full-scale collision test of the real car; then the force element structure of the rail holding mechanism was equated; finally, the vertical lift and the pitch angle of the three characteristic sections of car body and the wheelsets were used as the evaluation indicators to study the effects of the three design parameters: the gap distance (x1), the linear stage distance (Δ x2) and the stiffness of linear stage (k1). The results show that the linear stage distance has little influence on the collision attitude of the car body, while the x1 and k1 had a greater influence on the collision attitude of the car body. The reasonable reduction of the gap distance x1 and increase the k1 can effectively reduce the vertical lift of the wheelsets and alleviate the nodding phenomenon of the train, and reduce the derailment and jumping phenomenon during the train collision.

地铁是城市公共交通重要部分,碰撞事故一旦发生,将会造成巨大伤亡.本文建立车厢-乘员-扶手耦合三维多刚体动力学模型与头-地板耦合三维有限元模型,基于生物力学指标,研究3种基本工况下不同站姿地铁乘员头部损伤,旨在探究乘员姿态对头部损伤影响规律.结果表明:1)头部损伤生物力学指标最大值均基本集中在脑干处与颅脑顶端,可以判断发生脑损伤;2)地铁站立乘员抬头角度、面向方向对头部损伤影响较大,脚部竖跨角度对头部损伤结果有一定影响,而脚步横跨角度对头部损伤结果影响不大;3)地铁站立乘员保持头部弯曲角度45°、面向地铁行驶方向、脚步竖跨角度0°姿态,将在地铁碰撞事故中面临更严重脑损伤风险.

With the development of the subway and the pressing demand of environmentally friendly transportation, more and more people travel by subway. In recent decades, the issues about passenger passive safety on the train have received extensive attention. In this research, the head injury of a standing passenger in the subway is investigated. Three MADYMO models of the different standing passenger postures, defined as baseline scenarios, are numerically set up. HIC15 values of passengers with different postures are gained by systematic parametric studies. The injury numerical simulation results of various scenarios with different friction coefficients, collision acceleration, standing angle, horizontal handrail height, and ring handrail height are analyzed. Results show that the horizontal handrail provides better protection in the three different standing passenger postures. Different friction coefficients and the standing angle have great impact on the head injuries of passengers in three different scenarios. The handrail height also has some effects on head injury of passengers with different standing postures, so it is necessary to be considered when designing the interior layout of the subway. This study may provide guidance for the safety design of the subway and some advices for standing subway passengers.