Optimization for Crashworthiness of Urban Transit Trains Using Genetic Algorithm

As it is tough for the current energy absorb devices of urban vehicles to meet the crashworthiness requirements in the collision scenario of 25km/h, a methodology to improve the general crashworthiness is presented. A multi-criteria optimization, with the deformations and accelerations of all cars as the design functions and the force characteristics of end structures of cars as design variables, is defined and the Pareto Fonts are obtained. Then defining energy absorbed as design function, a single criteria optimization is made and the specific goal is achieved. No explicit relationship could be found between the design variables and the design functions, so a crash model of a train with velocity of 25km/h colliding to another train stopped is built and the genetic algorithm is chosen to solve the optimization problems. The results indicate that the crashworthiness performance of the trains is significantly improved and the crashworthiness requirements could be reached finally.