Component-based model to predict aerodynamic noise from high-speed train pantographs

At typical speeds of modern high-speed trains the aerodynamic noise produced by the airflow over the pantograph is a significant source of noise. Although numerical models can be used to predict this they are still very computationally intensive. A semi-empirical component-based prediction model is...

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Bibliographic Details
Published in:Journal of sound and vibration 2017-04, Vol.394, p.280-305
Main Authors: Latorre Iglesias, E., Thompson, D.J., Smith, M.G.
Format: Article
Language:English
Subjects:
Aerodynamic noise
Aerodynamics
Air flow
Component-based
Computational fluid dynamics
Cross-sections
Cylinders
High speed rail
High-speed train
Mathematical models
Noise
Noise control
Noise pollution
Noise prediction
Pantograph
Pantographs
Prediction
Railway
Semi-empirical
Sound
Struts
Studies
Trains
Turbulence
Wind tunnels
Yaw
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Summary:At typical speeds of modern high-speed trains the aerodynamic noise produced by the airflow over the pantograph is a significant source of noise. Although numerical models can be used to predict this they are still very computationally intensive. A semi-empirical component-based prediction model is proposed to predict the aerodynamic noise from train pantographs. The pantograph is approximated as an assembly of cylinders and bars with particular cross-sections. An empirical database is used to obtain the coefficients of the model to account for various factors: incident flow speed, diameter, cross-sectional shape, yaw angle, rounded edges, length-to-width ratio, incoming turbulence and directivity. The overall noise from the pantograph is obtained as the incoherent sum of the predicted noise from the different pantograph struts. The model is validated using available wind tunnel noise measurements of two full-size pantographs. The results show the potential of the semi-empirical model to be used as a rapid tool to predict aerodynamic noise from train pantographs.
ISSN:0022-460X
1095-8568
DOI:10.1016/j.jsv.2017.01.028