Simulating a Gaussian random process by conditional PDF

• Published in: Engineering computations Vol. 28; no. 5; pp. 540 - 556
• Main Authors: Klemenc, Jernej, Fajdiga, Matija
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 01-01-2011
• Subjects: Analysis; Computer simulation; Dynamic loads; Dynamical systems; Dynamics; Gaussian; Load; Loads (forces); Methods; Normal distribution; Probability density functions; Probability distribution; Product development; Random processes; Random variables; Simulation; Standard deviation; Structural engineering; Studies; Time series; Gaussian random process; Gaussian processes; Structural dynamic loads; Durability; Product development; Structural engineering; Excitation signal reconstruction; Conditional PDF; Dynamic loading
• ISSN: 0264-4401; 1758-7077
• DOI: 10.1108/02644401111141000

Purpose - One of the biggest problems in an R&D process is the acquisition of information about the structure dynamic loads, which are needed to reliably prove the structure's durability. This paper aims to present an innovative method for simulating stationary Gaussian random processes, which is based on the conditional probability density function (PDF) approach.Design methodology approach - The basic information on the structure dynamic loads is first obtained by short-duration measurements on prototypes or the structure itself. These data are then used to simulate the expected structure load states during operations. A theoretical background is presented first, which is followed by the application of the method.Findings - The results show that the spectral characteristics of the original and simulated Gaussian random processes are very similar, if the influential range of the conditional PDF is properly chosen.Practical implications - The method can be applied for simulating random loads of structures, and excitations of dynamic systems, for example.Originality value - The innovative simulation approach could be helpful to engineers in the early phases of the new product development process.