Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An efficient multifidelity ℓ1-minimization method for sparse polynomial chaos
Hydraulic Machinery Research Institute, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Hydraulic Machinery Research Institute, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics. Water Power Laboratory, Norwegian University of Science and Technology, Trondheim, Norway.ORCID iD: 0000-0001-7599-0895
Hydraulic Machinery Research Institute, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2018 (English)In: Computer Methods in Applied Mechanics and Engineering, ISSN 0045-7825, E-ISSN 1879-2138, Vol. 334, p. 183-207Article in journal (Refereed) Published
Abstract [en]

The Polynomial Chaos Expansion (PCE) methodology is widely used for uncertainty quantification of stochastic problems. The computational cost of PCE increases exponentially with the number of input uncertain variables (known as curse of dimensionality). Therefore, use of PCE for uncertainty quantification of industrial applications with large number of uncertain variables is challenging. In this paper, a novel methodology is presented for efficient uncertainty quantification of stochastic problems with large number of input random variables. The proposed method is based on PCE with combination of ℓ1-minimization and multifidelity methods. The developed method employs the ℓ1-minimization method to recover important coefficients of PCE using low-fidelity computations. The low-fidelity evaluations should be accurate enough to capture the physical trends well. After that the multifidelity PCE method is utilized to correct a subset of recovered coefficients using high-fidelity computations. A threshold parameter is defined in order to select the subset of recovered coefficients to be corrected. Two challenging analytical and CFD test cases namely, the Ackley function and the transonic RAE2822 airfoil with combined operational and geometrical uncertainties are considered to examine the performance of the methodology. It is shown that the proposed method can reproduce accurate results with much lower computational cost than the classical full Polynomial Chaos (PC), and ℓ1-minimization methods. It is observed that for the considered examples, the present method can achieve comparable accuracy with respect to the full PC and the ℓ1-minimization methods with significantly lower number of samples.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 334, p. 183-207
National Category
Fluid Mechanics and Acoustics
Research subject
Fluid Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-67825DOI: 10.1016/j.cma.2018.01.055ISI: 000431197000009Scopus ID: 2-s2.0-85042322160OAI: oai:DiVA.org:ltu-67825DiVA, id: diva2:1187240
Note

Validerad;2018;Nivå 2;2018-03-02 (svasva)

Available from: 2018-03-02 Created: 2018-03-02 Last updated: 2018-06-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Cervantes, Michel J.

Search in DiVA

By author/editor
Cervantes, Michel J.
By organisation
Fluid and Experimental Mechanics
In the same journal
Computer Methods in Applied Mechanics and Engineering
Fluid Mechanics and Acoustics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 16 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf