Advanced computational technique based on kriging and Polynomial Chaos Expansion for structural stability of mechanical systems with uncertainties

In this paper, a numerical strategy based on the combination of the kriging approach and the Polynomial Chaos Expansion (PCE) is proposed for the prediction of buckling loads due to random geometric imperfections and fluctuations in material properties of a mechanical system. The original computatio...

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Bibliographic Details
Published inJournal of engineering mathematics Vol. 130; no. 1; pp. 1 - 19
Main Authors Denimal, E., Sinou, J.-J.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.10.2021
Springer Nature B.V
Springer Verlag
Subjects
Applications of Mathematics
Axial compression
Buckling
Computational efficiency
Computational Mathematics and Numerical Analysis
Computing time
Engineering Sciences
Material properties
Mathematical and Computational Engineering
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Mechanical engineering
Mechanical systems
Mechanics
Parametric analysis
Polynomials
Random/Stochastic Differential Equations in Engineering Problems
Stiffness
Structural mechanics
Structural stability
Theoretical and Applied Mechanics
Rayleigh–Ritz
Structural stability
Uncertainties
Buckling
Kriging
Polynomial Chaos Expansion
Rayleigh-Ritz
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Summary:In this paper, a numerical strategy based on the combination of the kriging approach and the Polynomial Chaos Expansion (PCE) is proposed for the prediction of buckling loads due to random geometric imperfections and fluctuations in material properties of a mechanical system. The original computational approach is applied on a beam simply supported at both ends by rigid supports and by one punctual spring whose location and stiffness vary. The beam is subjected to a deterministic axial compression load. The PCE-kriging meta-modelling approach is employed to efficiently perform a parametric analysis with random geometrical and material properties. The approach proved to be computationally efficient in terms of number of model evaluations and in terms of computational time to predict accurately the buckling loads of a beam system. It is demonstrated that the buckling loads are substantially impacted not only by both the location and the stiffness of the spring, but also by the random parameters.
ISSN:0022-0833
1573-2703
DOI:10.1007/s10665-021-10157-9