Multi-objective optimization and sensitivity analysis of forced-steering bogies using surrogate models and NSGA-III

This research proposes a multi-objective optimization methodology to enhance the curving and tangent performance of forced-steering passenger trains. Using the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), we optimize a set of parameters — primary suspension stiffness (longitudinal and lateral) and steering linkage — to minimize wheelset unloading, derailment risk, rail rollover risk, and car body lateral acceleration. A 167-degree-of-freedom high-fidelity simulation model of the train is developed and validated against conventional rail vehicle data. Sensitivity analysis via the Sobol’ method identifies key design parameters, reducing the number of variables for optimization. A Kriging surrogate model is then employed to approximate the simulation model, making optimization feasible. Post-optimization, the robustness of the Pareto optimal solutions is evaluated under varying track conditions. Key findings reveal that steering ratio and longitudinal primary suspension stiffness are critical, while yoke-to-yoke parameters have minimal impact. The optimization results show a trade-off between curving performance and car body lateral acceleration, with solutions varying based on lateral stiffness. Two out of four Pareto optimal sets demonstrated improved robustness under varying curve radii and equivalent conicity, while all Pareto optimal sets exhibit equal robustness and significant improvements in performance under varying track friction. These findings emphasize the importance of robust design optimization across different operational conditions to achieve balanced performance.