Small-scale model test is an economical and efficient method to study the collision process analysis and crashworthiness design of full-scale high-speed trains. For high-speed trains, the vehicle body thickness of scaled train models is too small to be processed, resulting in thickness distortion. Although the distorted model can predict dynamic responses of the full-scale model, there are some errors in predicting the dynamic response parameters. This article proposes a new similitude distortion method to improve the prediction accuracy of the high-speed train body distorted model. First, the complete similitude relationship for train collisions was derived using dimensional analysis. According to the Buckingham π theory, the theoretical expression between prediction coefficient and distortion coefficient was obtained when the high-speed train thickness distortion occurred. Then, based on a full-scale high-speed train body prototype, the benchmark model and distorted model were established. Numerical simulation was used to explore the relationship between prediction coefficient and distortion coefficient. Finally, the distorted similar model was established using the similitude distortion method. The accuracy and feasibility of this approach were verified by comparing and analysing the dynamic response curves obtained by numerical simulations between the distorted similar model and prototype. The errors of the maximum displacement and deceleration were 2.70% and 8.26%. The results show that the similitude distortion method is reliable to relate dynamic responses of scaled models to the prototype when the vehicle body thickness distorts.


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    Title :

    Investigation on similitude method for dynamic response of high-speed train body considering thickness distortion


    Additional title:

    S. Lu et al.
    International Journal of Crashworthiness


    Contributors:
    Lu, Sisi (author) / Wang, Pan (author) / Yan, Kaibo (author) / Ni, Weitao (author) / Bao, Hongli (author) / Zhu, Wenqing (author)

    Published in:

    Publication date :

    2024-11-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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