 Assessing random field models in finite element analysis: a case studyVincent De Groof and Michael Oberguggenberger International Journal of Reliability and Safety, 2014, vol. 8, issue 2/3/4, 117-134 Abstract: The buckling load of thin-walled structures is sensitive to the presence of imperfections. Random field models constitute an established approach to accounting for these imperfections in numerical computations. Current evaluations of finite element models including random fields are usually done by comparison with the experimental buckling loads. This paper proposes an alternative approach to account for discrepancies inherent to numerical modelling. We use this to judge the shape of fitted correlation functions and the sensitivity of the computed buckling load with respect to the number of eigenvectors included in the expansion of the random field. Further, the applicability of fitted model covariance functions is contrasted with a principal component analysis approach as well as with artificially created local random imperfections. The results show that random field models have to be used with greatest care in order to avoid erroneous predictions of the buckling load. Keywords: random field models; geometrical imperfections; buckling load; thin-walled structures; Karhunen-Loève expansion; finite element analysis; case study; FEA; numerical modelling; random fields; eigenvectors; fitted model covariance; principal component analysis; PCA. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijrsaf:v:8:y:2014:i:2/3/4:p:117-134 Access Statistics for this article More articles in International Journal of Reliability and Safety from Inderscience Enterprises Ltd |
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