A comparative study for selecting and using simulation methods of Gaussian random surfaces

Conventional methods for generating Gaussian random surfaces, including the moving average (MA) time series model with nonlinear conjugate gradient method (NCGM), two-dimensional (2-D) digital filter method, and spectral representation method (SRM), are implemented with a wide range of autocorrelation length and truncation length values of the autocorrelation function (ACF). The ACF, power spectral density function (PSDF), and essential roughness parameters of the simulated surfaces are calculated and compared. Based on the simulation results, the mechanism of the truncation length of ACF affecting the simulated surfaces can be summarized as that the step formed by truncating ACF is not sufficiently small, thus resulting in non-negligible errors in the corresponding PSDF. Such errors will be propagated to the simulated surfaces. A conservative criterion is proposed to avoid the adverse effects of truncating ACF: make the autocorrelation length less than 8% of the surface dimensions and the truncation length at least seven times autocorrelation length. The results show that the MA model with NCGM overestimates the PSDF values of simulated surfaces in the high-frequency region, meaning significant high-frequency noise in the simulated surfaces. The 2-D digital filter method and the SRM have almost the same performance, and both methods are better than the MA model with NCGM when the criterion of truncating ACF is fulfilled. The SRM generates rough surfaces with the smallest standard deviation in terms of the roughness parameters, ACF, and PSDF in most cases, meaning that it can generate accurate surfaces at every single simulation and is more stable and efficient. Therefore, the SRM is the most recommended method among the three methods studied.