A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic vector process by a translation process with applications in wind velocity simulation

2013 ◽  
Vol 31 ◽  
pp. 19-29 ◽  
Author(s):  
M.D. Shields ◽  
G. Deodatis
Keyword(s):  
Wind Velocity ◽  
Translation Process ◽  
Vector Process ◽  
Non Gaussian ◽  
Stochastic Vector

scholarly journals On the Multiplicity of a Stochastic Vector Process

1975 ◽  
Vol 12 (S1) ◽  
pp. 187-194
Author(s):  
Harald Cramér
Keyword(s):  
One Dimensional ◽  
The Past ◽  
Vector Process ◽  
Stochastic Vector ◽  
General Conditions

This note deals with a q-dimensional stochastic vector process x(t) = {x1 (t), …, xq (t)}, satisfying certain stated general conditions. For such a process, there is a representation (1) in terms of stochastic innovations acting throughout the past of the process. The number N of terms in this representation is called the multiplicity of the x(t) process, and is uniquely determined by the process. For a one-dimensional process (q = 1) it is known that under certain conditions we have N = 1. For an arbitrary value of q, this note gives conditions under which we have N ≦ q.

Simulating a non-Gaussian vector process with application in hydrology

2014 ◽  
Vol 41 (6) ◽  
pp. 621-626 ◽  
Author(s):  
A. V. Frolov ◽  
T. Yu. Vyruchalkina ◽  
I. V. Solomonova
Keyword(s):  
Gaussian Vector ◽  
Vector Process ◽  
Non Gaussian

A Simulation Method for Non-Gaussian Rough Surfaces Using Fast Fourier Transform and Translation Process Theory

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Yuechang Wang ◽  
Ying Liu ◽  
Gaolong Zhang ◽  
Yuming Wang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Rough Surface ◽  
Tribological Behavior ◽  
Rough Surfaces ◽  
Simulation Method ◽  
Process Theory ◽  
Asperity Contact ◽  
Translation Process ◽  
Non Gaussian

The simulated rough surface with desired parameters is widely used as an input for the numerical simulation of tribological behavior such as the asperity contact, lubrication, and wear. In this study, a simulation method for generating non-Gaussian rough surfaces with desired autocorrelation function (ACF) and spatial statistical parameters, including skewness (Ssk) and kurtosis (Sku), was developed by combining the fast Fourier transform (FFT), translation process theory, and Johnson translator system. The proposed method was verified by several numerical examples and proved to be faster and more accurate than the previous methods used for the simulation of non-Gaussian rough surfaces. It is convenient to simulate the non-Gaussian rough surfaces with various types of ACFs and large autocorrelation lengths. The significance of this study is to provide an efficient and accurate method of non-Gaussian rough surfaces generation to numerically simulate the tribological behavior with desired rough surface parameters.

Estimating extremes of combined two Gaussian and non-Gaussian response processes

2014 ◽  
Vol 14 (03) ◽  
pp. 1350076 ◽  
Author(s):  
K. Gong ◽  
X. Z. Chen
Keyword(s):  
Gaussian Process ◽  
Gaussian Processes ◽  
New Combination ◽  
Parameter Study ◽  
Stochastic Dynamic ◽  
Translation Process ◽  
Combination Rules ◽  
Dynamic Loadings ◽  
Non Gaussian ◽  
Vector Valued

Assessment of structural performance under stochastic dynamic loadings requires estimation of the extremes of stochastic response components and the resultant responses as their linear and nonlinear combinations. This paper addresses the evaluations and combination rules for the extremes of scalar and vectorial resultant responses from two response components that may show non-Gaussian characteristics. The non-Gaussian response process is modeled as a translation process from an underlying Gaussian process. The mean crossing rates and extreme value distributions of resultant responses are calculated following the theory for vector-valued Gaussian processes. An extensive parameter study is conducted concerning the influence of statistical moments of non-Gaussian response components on the extremes of resultant responses. It is revealed that the existing combination rules developed for Gaussian processes are not applicable to the case of non-Gaussian process. New combination rules are suggested that permit predictions of the extremes of resultant responses directly from the extremes of response components.

Sign in / Sign up

Export Citation Format

Share Document