Open Access

ARTICLE

An Uncertainty Analysis Method for Artillery Dynamics with Hybrid Stochastic and Interval Parameters

Liqun Wang¹, Zengtao Chen², Guolai Yang^1,*

1 School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing, 210094, China
2 Department of Mechanical Engineering, University of Alberta, Edmonton, T6G 1H9, Canada

* Corresponding Author: Guolai Yang. Email:

Computer Modeling in Engineering & Sciences 2021, 126(2), 479-503. https://doi.org/10.32604/cmes.2021.011954

Received 07 June 2020; Accepted 14 September 2020; Issue published 21 January 2021

Abstract

This paper proposes a non-intrusive uncertainty analysis method for artillery dynamics involving hybrid uncertainty using polynomial chaos expansion (PCE). The uncertainty parameters with sufficient information are regarded as stochastic variables, whereas the interval variables are used to treat the uncertainty parameters with limited stochastic knowledge. In this method, the PCE model is constructed through the Galerkin projection method, in which the sparse grid strategy is used to generate the integral points and the corresponding integral weights. Through the sampling in PCE, the original dynamic systems with hybrid stochastic and interval parameters can be transformed into deterministic dynamic systems, without changing their expressions. The yielded PCE model is utilized as a computationally efficient, surrogate model, and the supremum and infimum of the dynamic responses over all time iteration steps can be easily approximated through Monte Carlo simulation and percentile difference. A numerical example and an artillery exterior ballistic dynamics model are used to illustrate the feasibility and efficiency of this approach. The numerical results indicate that the dynamic response bounds obtained by the PCE approach almost match the results of the direct Monte Carlo simulation, but the computational efficiency of the PCE approach is much higher than direct Monte Carlo simulation. Moreover, the proposed method also exhibits fine precision even in high-dimensional uncertainty analysis problems.

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