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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 104
Edited by: J. Pombo
Paper 7

Vibroacoustic Problems in High Speed Trains

F. Sorribes-Palmer, G. Alonso and A. Sanz-Andres

IDR/UPM, E.T.S.I. Aeronáuticos, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros, Madrid, Spain

Full Bibliographic Reference for this paper
F. Sorribes-Palmer, G. Alonso, A. Sanz-Andres, "Vibroacoustic Problems in High Speed Trains", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 7, 2014. doi:10.4203/ccp.104.7
Keywords: high speed train, vibro-acoustics, airborne noise, noise prediction.

Passengers comfort in terms of acoustic noise levels is a key train design parameter, especially relevant in high speed trains, where the aerodynamic noise is dominant. The aim of the work, described in this paper, is to make progress in the understanding of the flow field around high speed trains in an open field, which is a subject of interest for many researchers with direct industrial applications, but also the critical configuration of the train inside a tunnel is studied in order to evaluate the external loads arising from noise sources of the train. The airborne noise coming from the wheels (wheelrail interaction), which is the dominant source at a certain range of frequencies, is also investigated from the numerical and experimental points of view. The numerical prediction of the noise in the interior of the train is a very complex problem, involving many different parameters: complex geometries and materials, different noise sources, complex interactions among those sources, broad range of frequencies where the phenomenon is important, etc. During recent years a research plan is being developed at IDR/UPM (Instituto de Microgravedad Ignacio Da Riva, Universidad Politécnica de Madrid) involving both numerical simulations, wind tunnel and full-scale tests to address this problem. Comparison of numerical simulations with experimental data is a key factor in this process.

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