Aerodynamic noise becomes a significant source of railway noise at speeds above 300 km/h. The bogie of a high-speed train area is one of the most relevant aerodynamic noise sources. Semi-empirical component-based models have been used for bogie aerodynamic noise prediction because they allow fast and cheap calculations compared with numerical methods. A semi-empirical component-based model to predict high-speed train bogie aerodynamic noise is being developed based on an existing model already used to predict pantograph noise. Thus, noise tests were conducted in the Railway Technical Research Institute’s anechoic wind tunnel placed in Kunitachi for different flow speeds using simple shape-representing simplified bogie components and a simplified 1/5 scale bogie placed inside a cavity. The noise spectra radiated by a cube at different vertical positions and by two cubes in tandem are presented. These are used to adjust the empirical constants of the prediction model and assess the low-frequency interaction noise correction. The noise radiated by the complete simplified bogie and without some components are also presented, including the contribution of each bogie component to the overall noise.


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    Titel :

    Component-Based Model to Predict Aerodynamic Noise from High-Speed Train Bogies


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Sheng, Xiaozhen (Herausgeber:in) / Thompson, David (Herausgeber:in) / Degrande, Geert (Herausgeber:in) / Nielsen, Jens C. O. (Herausgeber:in) / Gautier, Pierre-Etienne (Herausgeber:in) / Nagakura, Kiyoshi (Herausgeber:in) / Kuijpers, Ard (Herausgeber:in) / Nelson, James Tuman (Herausgeber:in) / Towers, David A. (Herausgeber:in) / Anderson, David (Herausgeber:in)

    Kongress:

    International Workshop on Railway Noise ; 2022 ; Shanghai, China December 07, 2022 - December 09, 2022



    Erscheinungsdatum :

    24.02.2024


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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