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Development of an efficient multifidelity non-intrusive uncertainty quantification method
University of Tehran.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0001-7599-0895
University of Tehran.
2018 (English)In: Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems / [ed] E. Andrés-Pérez, L.M. González, J. Periaux, N. Gauger, D. Quagliarella, K. Giannakoglou, Springer, 2018, p. 483-497Chapter in book (Refereed)
Abstract [en]

Most engineering problems contain a large number of input random variables, and thus their polynomial chaos expansion (PCE) suffers from the curse of dimensionality. This issue can be tackled if the polynomial chaos representation is sparse. In the present paper a novel methodology is presented based on combination of -minimization and multifidelity methods. The proposed method employ the - minimization method to recover important coefficients of PCE using low-fidelity computations. The developed method is applied on a stochastic CFD problem and the results are presented. The transonic RAE2822 airfoil with combined operational and geometrical uncertainties is considered as a test case to examine the performance of the proposed methodology. It is shown that the new method can reproduce accurate results with much lower computational cost than the classical full Polynomial Choas (PC), and - minimization methods. It is observed that the present method is almost 15–20 times faster than the full PC method and 3–4 times faster than the classical -minimization method.

Place, publisher, year, edition, pages
Springer, 2018. p. 483-497
Series
Computational Methods in Applied Sciences (COMPUTMETHODS), ISSN 1871-3033 ; 49
National Category
Fluid Mechanics and Acoustics
Research subject
Fluid Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-68862DOI: 10.1007/978-3-319-89890-2_31Scopus ID: 2-s2.0-85053002099ISBN: 978-3-319-89890-2 (print)OAI: oai:DiVA.org:ltu-68862DiVA, id: diva2:1209425
Available from: 2018-05-23 Created: 2018-05-23 Last updated: 2019-01-14Bibliographically approved

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Cervantes, Michel

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CiteExportLink to record
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