Techno Press
Techno Press

Wind and Structures   Volume 18, Number 6, June 2014, pages 693-715
Simulation of non-Gaussian stochastic processes by amplitude modulation and phase reconstruction
Yu Jiang, Junyong Tao and Dezhi Wang

Abstract     [Full Text]
    Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.
Key Words
    stochastic simulation; non-Gaussian; amplitude modulation; phase reconstruction
Yu Jiang and Junyong Tao: Laboratory of Science and Technology on Integrated Logistics Support, College of Mechatronic Engineering & Automation, National University of Defense Technology, Changsha 410073, China
Dezhi Wang: Centre for Engineering Dynamics, University of Liverpool, Liverpool, UK

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