Simulation of a class of non-normal random processes

Abstract

This study addresses the simulation of a class of non-normal processes based on measured samples and sample characteristics of the system input and output. The class of non-normal processes considered here concerns environmental loads, such as wind and wave loads, and associated structural responses. First, static transformation techniques are used to perform simulations of the underlying Gaussian time or autocorrelation sample. An optimization procedure is employed to overcome errors associated with a truncated Hermite polynomial transformation. This method is able to produce simulations which closely match the sample process histogram, power spectral density, and central moments through fourth order. However, it does not retain the specific structure of the phase relationship between frequency components, demonstrated by the inability to match higher order spectra. A Volterra series up to second order with analytical kernels is employed to demonstrate the bispectral matching made possible with memory models. A neural network system identification model is employed for simulation of output when measured system input is available, and also demonstrates the ability to match higher order spectral characteristics.