Calculation of probability density functions for nonlinear vibration systems

Abstract

AbstractTechnical systems are subjected to a variety of excitations that cannot generally be described in deterministic ways. Random excitations such as road roughness, wind gusts or loads on marine structures are commonly described by stochastic differential equations (SDEs). Given a set of SDEs, the main task is in finding probability density functions (PDFs), which yield statistical information about the system states. Monte‐Carlo simulations represent a general way of generating PDFs, however, reliable integration methods can be time‐consuming for complex systems. An alternative way of finding PDFs lies in the analysis of the Fokker‐Planck equation, a partial differential equation of the PDF. Linear problems under Gaussian excitation can be solved analytically, which is the case only for a small class of nonlinear problems. As a result, there are a number of methods of approximating PDFs for general problems. Most of these are restricted to comparably low dimensions, such as d=4 ("curse of dimensionality"), limiting the relevance to technical applications. This paper presents solutions to problems of dimensions up to d=10, applying a Galerkin‐method that expands approximate solutions into orthogonal polynomials. Problems include polynomial nonlinearities in damping and restoring terms, such as classical Duffing‐elements, as well as other types of nonlinearities, demonstrated on a typical problem in vehicle dynamics. All results are compared with results from Monte‐Carlo simulations or exact solutions, where available. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)