Kriging-model-based uncertainty quantification in computational fluid dynamics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This paper proposes an efficient and accurate non-intrusive uncertainty quantification (UQ) method in computational fluid dynamics (CFD). Emphasis is placed on developing an UQ method that can accurately predict stochastic behaviors of output solution with small number of sampling simulations, and is also accurate for non-smooth output uncertainty responses. The proposed method is based on Kriging surrogate model, and the Kriging function values are used to evaluate output uncertainties robustly even with non-smooth responses, while using both the fit uncertainty and the gradient information of the Kriging predictors for dynamic adaptive sampling. The proposed Kriging-model-based UQ method shows a superior performance in estimating the non-smooth responses of output solution in terms of accuracy and robustness compared to the existing polynomial chaos expansion and the adaptive sampling method based on only the Kriging predictor fit uncertainty. The proposed method is first tested on analytical non-smooth functions under uniform uncertainties, and then applied to the transonic RAE 2822 airfoil flow under normal uncertainties in freestream Mach number by coupling the proposed UQ method with CFD.

Original languageEnglish
Title of host publication32nd AIAA Applied Aerodynamics Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781624102882
DOIs
Publication statusPublished - 2014
Event32nd AIAA Applied Aerodynamics Conference 2014 - Atlanta, GA, United States
Duration: 2014 Jun 162014 Jun 20

Publication series

Name32nd AIAA Applied Aerodynamics Conference

Other

Other32nd AIAA Applied Aerodynamics Conference 2014
CountryUnited States
CityAtlanta, GA
Period14/6/1614/6/20

ASJC Scopus subject areas

  • Aerospace Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Kriging-model-based uncertainty quantification in computational fluid dynamics'. Together they form a unique fingerprint.

Cite this