Dynamic modelling and multidisciplinary injury-based design optimization of driver workspace in high-speed train collisions

The driver workspace is the closest region to the collision zone, making it the most vulnerable to direct impact during a collision event. Designing optimal parameter configurations for this workspace is critical, as it plays a vital role in the passive safety protection system of trains, ensuring enhanced safety in industrial rail vehicle design and production. To minimize collision risks, this study establishes an authentic driver cabin dynamics model based on the MADYMO and implements a preference-based hybrid optimization strategy for Driver Workspace Layout Optimization (DWLOP). A coupled console-seat-dummy dynamic model employing three-dimensional acceleration profiles from train-to-train collisions as boundary conditions is developed to accurately simulate occupant dynamics in collision scenarios. A comprehensive driver collision injury evaluation index system was established by integrating the Weighted Injury Criteria (WIC), the UK AV/ST9001 standard, and the US FMVSS208 standard. A hybrid preference-based many-objective optimization algorithm strategy, namely S-VI, under interval 2-tuple linguistic sets combining Subspace Segmentation based Co-evolutionary Algorithm (SSCEA) and VIKOR, is proposed to solve the DWLOP. The injury metrics across different body regions are used as the optimization objective to minimize the damage to drivers. The performance comparison demonstrates SSCEA has superior performance in processing DWLOP compared to other algorithms. The optimization result shows significant improvements in head safety performance, with HIC reduced by 83.31 %, and a₃ₘₛ decreased by 54.61 %. The results confirm that the proposed S-VI optimization strategy offers substantial advantages in DWLOP. The integration of these techniques contributes to improve the passive safety protection management of trains and provide reference for the construction of the train production safety industry.