Methodology to optimize wedge suspensions of three-piece bogies of railway vehicles

Abstract

Wedge suspensions are critical systems for three-piece bogies. This paper proposes a methodology to optimize wedge suspensions using white-box suspension models, dynamic simulations of railway vehicle systems, parallel multi-objective Particle Swarm Optimization (pMOPSO), and parallel multi-objective Genetic Algorithm (pMOGA). Two types of original wedge suspensions with three different toe angle configurations were modeled and compared. Four case studies were carried out to prove the feasibility of the optimization methodology. A series of optimized designs were identified using the Pareto Front technique. Demonstrative optimized designs were compared with the original designs. Results show that wedge suspensions with the toe-in configuration provide better dynamic performance for freight wagons. Significant reductions to the maximum wheel/rail contact forces can be achieved by the optimized designs. Linear speed-up was achieved by using the parallel computing technique.