SIMULATION OF A CLASS OF NONNORMAL RANDOM-PROCESSES

This study addresses the simulation of a class of non-normal processes based on measured samples and sample characteristics of the system in put and output. The class of nonnormal processes considered here conce rns environmental loads, such as wind and wave loads, and associated s tructural responses. First, static transformation techniques are used to perform simulations of the underlying Gaussian time or autocorrelat ion sample. An optimization procedure is employed to overcome errors a ssociated with a truncated Hermite polynomial transformation. This met hod is able to produce simulations which closely match the sample proc ess histogram, power spectral density, and central moments through fou rth order. However, it does not retain the specific structure of the p hase relationship between frequency components, demonstrated by the in ability to match higher order spectra. A Volterra series up to second order with analytical kernels is employed to demonstrate the bispectra l matching made possible with memory models. A neural network system i dentification model is employed for simulation of output when measured system input is available, and also demonstrates the ability to match higher order spectral characteristics. Copyright (C) 1996 Elsevier Sc ience Ltd.