This paper deals with the analysis of the dynamic behavior of nonlinear systems subject to probabilistic uncertainties in the physical parameters. The Polynomial Chaos Expansion PCE is proposed to resolve this problem. The main objective is to estimate the probability distribution of the non linear dynamic response for a given dispersion of mass parameters. The method proposed allows notable performances in terms of efficiency and accuracy. This result comes from a comparison between the PCE performance with those offered by the Perturbation Method (PM) and the referential method Monte Carlo Simulation (MCS). This approach allows reducing the computational cost
Polynomial Chaos Expansions (PCEs) offer an efficient alternative to assess the statistical properti...
International audienceThis study aims at pointing out the somehow complex behavior of the structural...
International audienceMultidisciplinary analysis (MDA) is nowadays a powerful tool for analysis and ...
International audienceThe propagation of uncertain input parameters in a linear dynamic analysis is ...
In many fields, active research is currently focused on quantification and simulation of model uncer...
AbstractStructural uncertainties greatly influence the dynamic responses of engineering structures. ...
The generalized Polynomial Chaos Expansion Method (gPCEM), which is a random uncertainty analysis me...
Characterizing the time-domain response of a random multiple-degree-of-freedom dynamical system is c...
International audienceIn this work we address the problem of performing uncertainty and sensitivity ...
The first two moments of the steady-state response of a dynamical random system are determined throu...
A dynamical uncertain system is studied in this paper. Two kinds of uncertainties are addressed, whe...
The first two moments of the steady-state response of a dynamical random system are determined throu...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
Polynomial chaos solution for the frequency response of linear non-proportionally damped dynamic sys...
Frequency Response Functions (FRFs) are important for assessing the behavior of stochastic linear dy...
Polynomial Chaos Expansions (PCEs) offer an efficient alternative to assess the statistical properti...
International audienceThis study aims at pointing out the somehow complex behavior of the structural...
International audienceMultidisciplinary analysis (MDA) is nowadays a powerful tool for analysis and ...
International audienceThe propagation of uncertain input parameters in a linear dynamic analysis is ...
In many fields, active research is currently focused on quantification and simulation of model uncer...
AbstractStructural uncertainties greatly influence the dynamic responses of engineering structures. ...
The generalized Polynomial Chaos Expansion Method (gPCEM), which is a random uncertainty analysis me...
Characterizing the time-domain response of a random multiple-degree-of-freedom dynamical system is c...
International audienceIn this work we address the problem of performing uncertainty and sensitivity ...
The first two moments of the steady-state response of a dynamical random system are determined throu...
A dynamical uncertain system is studied in this paper. Two kinds of uncertainties are addressed, whe...
The first two moments of the steady-state response of a dynamical random system are determined throu...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
Polynomial chaos solution for the frequency response of linear non-proportionally damped dynamic sys...
Frequency Response Functions (FRFs) are important for assessing the behavior of stochastic linear dy...
Polynomial Chaos Expansions (PCEs) offer an efficient alternative to assess the statistical properti...
International audienceThis study aims at pointing out the somehow complex behavior of the structural...
International audienceMultidisciplinary analysis (MDA) is nowadays a powerful tool for analysis and ...