Abstract
Traditional methods for stiffness analysis of the air spring are based on deterministic assumption that the parameters are fixed. However, uncertainties have widely existed, and the mechanic property of the air spring is very sensitive to these uncertainties. To model the uncertainties in the air spring, the interval/random variables models are introduced. For response analysis of the interval/random variables models of the air spring system, a new unified orthogonal polynomial expansion method, named as sparse quadrature-based interval and random moment arbitrary polynomial chaos method (SQ-IRMAPC), is proposed. In SQ-IRMAPC, the response of the acoustic system related to both interval and random variables is approximated by the moment-based arbitrary orthogonal polynomial expansion. To efficiently calculate the coefficient of the interval and random orthogonal polynomial expansion, the sparse quadrature is introduced. The proposed SQ-IRMAPC was employed to analyze the mechanic performance of an air spring with interval and/or random variables, and its effectiveness has been demonstrated by fully comparing it with the most recently proposed orthogonal polynomial-based interval and random analysis method.
Highlights
Air springs are widely used for isolating vibrations to enhance comfort for the passengers in railway vehicles
To efficiently calculate stiffness of the air spring system with interval and random variables, a new interval and random polynomial chaos method named as Sparse Quadrature-based Interval and Random Moment Arbitrary Polynomial Chaos (SQIRMAPC) is proposed
In SQ-IRMAPC, the moment-based arbitrary polynomial chaos is used to approximate the response of interest
Summary
Air springs are widely used for isolating vibrations to enhance comfort for the passengers in railway vehicles. As the Moment-based Arbitrary Polynomial Chaos (MAPC) can avoid the fitting error introduced by the construction of the PDF for random variable, the convergence of the MAPC was significantly better than that of the conventional APC [10]. In these above random methods, a huge number of statistical data is needed to obtain the moment or PDF of random variables. To improve the accuracy of the polynomial chaos method for the hybrid uncertain structure-acoustic problem with complex probability distributions, the APC has been recently developed for hybrid interval and random analysis [31, 32]. Note that the main difference between the proposed method, the recently proposed interval, and random APC method is that different integration methods are used to calculate the expansion coefficient
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