Random vibration analysis of an uncertain vehicle-track coupled system based on a polynomial dimensional decomposition

ABSTRACT

In this paper, the uncertainty propagation of an uncertain vehicle-track coupled system (VTCS) subjected to track irregularity is quantified using a polynomial-dimensional decomposition (PDD). Firstly, the conditional power spectral density (PSD) of response related to uncertain parameters is derived using the pseudo-excitation method. Secondly, the PDD surrogate model that can describe the probabilistic characters of the uncertainty propagation is established by conducting the dimensional decomposition to the conditional PSD with component functions and performing the Fourier expansion to the component functions. Finally, the dimensional reduction integration and Gauss integration are introduced to overcome the difficulty of high-dimensional integration when calculating the expansion coefficients. In numerical example, the proposed method is applied to the uncertainty quantification of vertical vibration response of an uncertain VTCS, the accuracy and efficiency of the proposed method are verified by comparing the results of the PDD method with that of Monte-Carlo simulation.

KEYWORDS:

• Vehicle-track coupled dynamics
• uncertainty quantification
• polynomial dimensional decomposition
• pseudo-excitation method

Acknowledgement

The research work of this paper is supported by the National Natural Science Foundation of China (Nos. 11772084 and U1906233), the National High Technology Research and Development Program of China (No. 2017YFC0307203), and the Key Technology Research and Development Program of Shandong (No. 2019JZZY010801).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [11772084,U1906233]; National High Technology Research and Development Program of China [2017YFC0307203]; Key Technology Research and Development Program of Shandong [2019JZZY010801].