6533b81ffe1ef96bd12772db

RESEARCH PRODUCT

A novel method based on augmented Markov vector process for the time-variant extreme value distribution of stochastic dynamical systems enforced by Poisson white noise

Meng-ze LyuJianbing ChenAntonina PirrottaAntonina Pirrotta

subject

Numerical AnalysisMarkov chainDynamical systems theoryComputer scienceApplied MathematicsProbability density functionWhite noisePoisson distribution01 natural sciencesStochastic dynamic system010305 fluids & plasmassymbols.namesakeAugmented Markov vector proceJoint probability distributionModeling and Simulation0103 physical sciencesPoisson white noise excitationsymbolsGeneralized extreme value distributionApplied mathematicsSettore ICAR/08 - Scienza Delle Costruzioni010306 general physicsExtreme value theoryTime-variant extreme value process

description

Abstract The probability density function (PDF) of the time-variant extreme value process for structural responses is of great importance. Poisson white noise excitation occurs widely in practical engineering problems. The extreme value distribution of the response of systems excited by Poisson white noise processes is still not yet readily available. For this purpose, in the present paper, a novel method based on the augmented Markov vector process for the PDF of the time-variant extreme value process for a Poisson white noise driven dynamical system is proposed. Specifically, the augmented Markov vector (AMV) process is constructed by combining the extreme value process and its underlying response process. Then the joint probability density of the AMV can be evaluated by solving the Chapman-Kolmogorov Equation, e.g., via the path integral solution (PIS). Further, the PDF of the time-variant extreme value process is obtained, and can be used, say, to estimate the dynamic reliability of a stochastic system. For the purpose of illustration and verification, several numerical examples are studied and compared with Monte Carlo solution. Problems to be further studied are also discussed.

yearjournalcountryeditionlanguage
2020-01-01Communications in Nonlinear Science and Numerical Simulation
https://doi.org/10.1016/j.cnsns.2019.104974