Abstract
Reducing elastic vibration of railway vehicle carbodies is required to improve ride comfort. A high-accuracy numerical analysis model is required to study effective vibration reduction methods. This paper describes a new numerical analysis model, and a method for determining parameters for the proposed model by using particle swarm optimization. After the application of the proposed method to a Shinkansen-type test vehicle, the authors created an analytical model with a maximum natural frequency difference between measured and calculated results of the targeted six elastic vibration modes within 0.86%, which indicates the effectiveness of the proposed model and parameter determination method.
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