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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (18): 229923.doi: 10.7527/S1000-6893.2024.29923

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles    

Fast algorithm for non-Gaussian stochastic processes based on translation processes

Jinming LIU1,2, Xing TAN1,2, Weiting CHEN1,2, Huan HE1,2()   

  1. 1.State Key Laboratory of Mechanics and Control for Aerospace Structures,Nanjing University of Aeronautics and Astronautics,Nanjing  210016,China
    2.Institute of Vibration Engineering Research,Nanjing University of Aeronautics and Astronautics,Nanjing  210016,China
  • Received:2023-11-29 Revised:2023-12-13 Accepted:2024-01-24 Online:2024-02-06 Published:2024-02-02
  • Contact: Huan HE E-mail:hehuan@nuaa.edu.cn

Abstract:

In the non-Gaussian stationary random vibration environment testing technology, testing techniques based on probability density functions can more accurately reproduce the test environment. However, existing simulation algorithms based on probability density functions suffer from efficiency issues, making it challenging to apply them to engineering. A fast algorithm for simulating non-Gaussian stationary stochastic processes is proposed in this paper based on the translation process theory of stochastic processes. To address the difficulties and slow computation speed in critical double integral calculations of the field transformation theory, we expand the correlation coefficient functions of two random processes in series. This approach transforms a complicated double integral with an implicit function into a simple definite integral, which is efficiently solved using the Gauss-Hermite quadrature rule. Numerical simulations validate that this method can improve the efficiency of simulating non-Gaussian stationary random processes without compromising simulation accuracy, meeting the real-time requirement for generating random signals in the context of random vibration environment experiments.

Key words: random vibration testing, non-Gaussian stationary stochastic processes, stochastic signal simulation, translation processes, probability density functions

CLC Number: