Noise abatement generally comes at a cost while offering the benefit of an acoustically preferable solution. In the concept or (re-)design phase for a train, both costs and benefits must be accessible to take informed decisions. Various prediction methods are currently used in the rail industry, that all come with their own advantages and limitations. The Shift2Rail research project DESTINATE aims to support this by developing and advancing acoustic prediction methods. Within this project, an OTPA model was developed to predict interior noise in a light rail vehicle. Various options to utilize the OTPA model in predicting design changes were explored, the most promising one being the integration of FIR filters in the sound synthesis. OTPA results produce rankings for the main noise contributors as well as audible simulations of interior sound signals.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Shift2Rail Research Project DESTINATE Interior Railway Noise Prediction Based on OTPA


    Additional title:

    Notes Numerical Fluid Mech.




    Publication date :

    2021-04-09


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Shift2Rail Research Project DESTINATE Interior Railway Noise Prediction Based on OTPA

    Isert, Nathan / Martner, Otto | British Library Conference Proceedings | 2021


    DESTINATE - a Shift2Rail project on railway noise reduction methodologies

    Böhm, Jenny / Hecht, Markus / Baumgärtel, Yasmin | TIBKAT | 2017


    Utilizarea elicelor navale la echipamentele destinate epurarii biologice a apelor uzate, prin metoda aerarii

    Micu, D. / Institutui National de Cercetare Dezvoltare Turbomotoare COMOTI | British Library Conference Proceedings | 1998


    Vehicle Interior Noise Prediction

    Brandl, S. / Graf, B. / Biermayer, W. et al. | British Library Conference Proceedings | 2014