The Interior Noise Prediction Analysis On The High-speed Train

High-speed trains is the general trend of railway passenger transport in today's world. But, High-speed,high noise. High noise contrary to the spirit of people-oriented in product design. So noise control is imperative. Noise prediction in early car body design are important for reducing vehicle design cycle and design costs, improving design quality.First of all, I established a simplified high-speed train body model including a structural model, an acoustic model and an acoustic-solid coupling model. Then I carried out a modal analysis on structural model and acoustic-solid coupling model. And I also carried out a modal analysis on acoustic model having chair and acoustic model having chair and human body. After that,I compared the modal frequencies and modal shapes. The comparative results show that the natural frequencies of coupling mode lower than the structure of and cavity mode. Vibration shapes of coupling model correspond with the structure and the cavity mode shape.Second,I introduced the use of harmonic response in the train body dynamic analysis. I carried out analysis of the harmonic response on structural model of the train body and coupling model, compared the results. Then I extracted vibration displacement of the nodes in the coupling surface, loaded it on the boundary element model in SYSNOISE as boundary conditions, Calculated and drawn on the sound pressure level distribution in the face field points, and the sound pressure level frequency response characteristics of point sites. Sound pressure distribution shows that:With the increasing of frequency, noise-intensive region gradually moves from the central body to the end walls and the walls near the windows. In the frequency band of 200～400Hz, it also moves to the aisle of train.Finally, I carried out the random stimulus-response analysis on coupling model. I simulated the track random acceleration power spectral density with the use of MATLAB, loaded this PSD on the boundary element model in SYSNOISE as boundary conditions, calculated the sound pressure random response of the coupling model and got cloud maps of sound pressure level. Then exported the linear sound pressure level data into EXCEL, deal with it and got A-weighted sound pressure level curve in the points. The curve shows that the sound pressure level in two rows seat-side higher than that in three rows seat-side.And the sound pressure level on two sides higher than that in the train aisle In this paper, using the CAE technology,I studied train body vibration and in-train acoustic characteristics and forecasted the distribution of sound pressure which generated form body vibration, which laid a foundation for structural optimization and reducing vibration and noise...