Numerical optimization for vibration and noise of the wheel based on PSO-GA method

Abstract

Currently, those reported researches conducted optimal design for the wheel only in order to reduce the tread wear and increase the service life, but they did not consider the wheel vibration and radiation noise which seriously influence people’s life and did not achieve obvious noise reduction effects. Aiming at this question, a multi-body dynamic model of the high-speed train was established, and the vertical and radial force was extracted to input into the finite element model of the wheel to compute its vibration characteristics. Then, the wheel was conducted on a multi-objective optimization based on particle swarm optimization improved by genetic algorithm (PSO-GA) method. Finally, the optimized vibration results were mapped to the acoustic element model to compute the radiation noise of the wheel. The computational model was also validated by experimental test. In order to observe the optimized effect, the optimized results were compared with those of the traditional GA and PSO method. Solutions of the traditional GA and PSO method were relatively dispersed during iterations and the algorithm could easily fall into the locally optimal solution. The optimized results of PSO-GA method were obviously better. Compared with the original wheel, the vibration acceleration was reduced by 22.9 %, and the mass was reduced by 1.1 %. Finally, the optimized vibration was mapped to the boundary element model to compute the radiation noise of the wheel, and the computational results were compared with the original wheel. Radiation noises of the original wheel were obviously more than that of the optimized wheel, and there were a lot of obvious peak noises in the original wheel. Radiation noises of the optimized wheel only had two obvious noise peaks in the analyzed frequency. Therefore, a wheel with low noises and lightweight was achieved in this paper.