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In this paper, the dynamic stress of the front subframe of a passenger car was obtained using modal stress recovery method to estimate the fatigue life. A finite element model of the subframe was created and its accuracy was checked by modal test in a free hanging state. Furthermore, the whole vehicle rigid-flexible coupling model of the passenger car was built up while taking into account the flexibility of the subframe. Meanwhile, the road test data was used to verify the validity of the dynamic model. On this basis, the modal displacement time histories of the subframe were calculated by a dynamic simulation on virtual proving ground consisting of Belgian blocks, cobblestone road and washboard road. By combining the modal displacement time histories with modal stress tensors getting from normal mode analysis, the dynamic stress time histories of the subframe were obtained through modal stress recovery method.

This paper proposes a new method of predicting the sound absorption performance of polymer wool using artificial neural networks (ANN) model. Some important parameters of the proposed model have been adjusted to best fit the non-linear relationship between the input data and output data. What's more, the commonly used multiple non-linear regression model is built to compare with ANN model in this study. Measurements of the sound absorption coefficient of polymer wool based on transfer function method are also performed to determine the sound absorption performance according to GB/T18696. 2-2002 and ISO10534- 2: 1998 (E) standards. It is founded that predictions of the new model are in good agreement with the experiment results.

In recent years, the interior noise of automobile has been becoming a significant problem. In order to reduce the noise, porous materials have been widely applied in automobile manufacturing. In this study, the simulation method and optimal analysis are used to determine the optimum sound absorption of polyurethane foam. The experimental simulation is processed based on the Johnson-Allard model. In the model, the foam adheres to a hard wall. The incident wave is plane wave. The function of the model is to calculate the noise reduction coefficient of polyurethane foam with different thickness, density and porosity. The back propagation neural network coupled with genetic optimization technique is utilized to predict the optimum sound absorption. A developed back propagation neural network model is trained and tested by the simulation data.