A Mixed Uncertainty Quantification Approach using Evidence Theory and Stochastic Expansions

Abstract

Uncertainty quantification (UQ) is the process of quantitative characterization and propagation of input uncertainties to the response measure of interest in experimental and computational models. The input uncertainties in computational models can be either aleatory, i.e., irreducible inherent variations, or epistemic, i.e., reducible variability which arises from lack of knowledge. Previously, it has been shown that Dempster Shafer theory of evidence (DSTE) can be applied to model epistemic uncertainty in case of uncertainty information coming from multiple sources. The objective of this paper is to model and propagate mixed uncertainty (aleatory and epistemic) using DSTE. In specific, the aleatory variables are modeled as Dempster Shafer structures by discretizing them into sets of intervals according to their respective probability distributions. In order to avoid excessive computational cost associated with large scale applications, a stochastic response surface based on point-collocation non-intrusive polynomial chaos has been implemented as the surrogate model for the response. A convergence study for accurate representation of aleatory uncertainty in terms of minimum number of subintervals required is presented. The mixed UQ approach is demonstrated on a numerical example and high fidelity computational fluid dynamics study of transonic flow over RAE 2822 airfoil.

Department(s)

Mechanical and Aerospace Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Computational Fluid Dynamics; Evidence Theory; Point-Collocation; Polynomial Chaos; Representation of Uncertainty; Uncertainty Quantification

International Standard Serial Number (ISSN)

2152-5080

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Begell House Inc., All rights reserved.

Publication Date

01 Jan 2015

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