Abstract
Increasing the speed of high-speed trains requires the lightweight design of vehicles to meet the economic and ecological efficiency requirements of such trains. However, these objectives conflict with the interior noise control in high-speed trains because the sound insulation of panel structures follows the mass law principle. The train floor, the main train body structure of the high-speed train, is vital for interior noise control because its sound insulation performance directly affects the interior noise levels. Owing to the complexity of the composite floor system, reliable measurement and accurate estimation of its sound insulation performance are often time-consuming and laborious. To address this situation, this study proposes an artificial neural network (ANN)-based model to predict the sound insulation characteristics of a composite floor. First, a sound insulation model of the composite floor is built based on statistical energy analysis (SEA). The sound insulation performance of 200 cases of composite floors is calculated by varying the dimensions of the extruded floor, thickness of the webs, sound-absorbing material, and wooden floor to formulate a sound insulation database of composite floors. Next, an ANN model is introduced and trained on the sound insulation database. The sound insulation prediction results obtained using the ANN model are compared to the prediction results obtained using the experiment to validate its effectiveness. Subsequently, the NSGA-II optimization method is used to optimize the sound insulation structure of the composite floor. Compared with the regular composite floor structure, the optimized structure reduced the mass of the composite floor by 10.93 kg and increased the weight of the sound insulation ( Rw) by 6.3 dB. The proposed method can be an effective, economical, and efficient tool for vehicle designers and can help promote the sound insulation optimization design of high-speed train composite floors.
Published Version
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Schedule a callMore From: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
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