Time-Domain Response of Damped Stochastic Multiple-Degree-of-Freedom Systems

E Jacquelin, D Brizard (Universite Claude Bernard Lyon, France), S Adhikari & MI Friswell (Swansea University)

ASCE Journal of Engineering Mechanics, Vol. 146, No. 1, January 2020, paper 06019005

Abstract

Characterizing the time-domain response of a random multiple-degree-of-freedom dynamical system is challenging and often requires Monte Carlo simulation (MCS). Differential equations must therefore be solved for each sample, which is time consuming. This is why polynomial chaos expansion (PCE) has been proposed as an alternative to MCS. However, it turns out that PCE is not adapted to simulate a random dynamical system for long-time integration. Recent studies have shown similar issues for the steady-state response of a random linear dynamical system around the deterministic eigenfrequencies. A Pade approximant approach has been successfully applied; similar interesting results were also observed with a random mode approach. Therefore the latter two methods were applied to a random linear dynamical system excited by a dynamic load to estimate the first two statistical moments and the probability density function at a given instant of time. Whereas the random modes method has been very efficient and accurate to evaluate the statistics of the response, the Pade approximant approach has given very poor results when the coefficients were determined in time domain. However, if the differential equations were solved in the frequency domain, the Pade approximants, which were also calculated in the frequency domain, provided results in excellent agreement with the MCS results.

Paper Availability

This material has been published in the ASCE Journal of Engineering Mechanics, Vol. 146, No. 1, January 2020, paper 06019005. Unfortunately the copyright agreement with ASCE does not allow for the PDF file of the paper to be available on this website.


Link to paper using doi: 10.1061/(ASCE)EM.1943-7889.0001705

Link to the Journal of Engineering Mechanics website