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Efficiency of Uncertainty Propagation Methods for Estimating Output Moments

Journal Article · · Computer Aided Chemical Engineering
 [1];  [2]
  1. Auburn University, Auburn, AL (United States); Auburn University
  2. Auburn University, Auburn, AL (United States)
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Uncertainty propagation methods are used to estimate the distribution of model outputs resulting from a set of uncertain model outputs. There are a number of uncertainty propagation methods available in literature. This paper compares six non-intrusive uncertainty propagation methods, Latin Hypercube Sampling, Full Factorial Integration, Univariate Dimension Reduction, Halton series, Sobol series, and Polynomial Chaos Expansion, in terms of their efficiency for estimating the first four moments of the output distribution using computational experiments. Here, the results suggest employing FFNI if there are few uncertain inputs, up to three. Uncertainty propagation methods that utilize Halton and Sobol series are found to be robust for estimating output moments as the number of uncertain inputs increases. In general, higher order polynomial chaos expansion approximations (3rd-5th order) obtained accurate estimates of model outputs with fewer model evaluations.
Research Organization:
RAPID Manufacturing Institute, New York, NY (United States)
Sponsoring Organization:
NSF; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office
Grant/Contract Number:
EE0007888
OSTI ID:
1642445
Journal Information:
Computer Aided Chemical Engineering, Journal Name: Computer Aided Chemical Engineering Vol. 47; ISSN 1570-7946
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (9)

An algorithm to determine sample sizes for optimization with artificial neural networks journal July 2012
A comparative study of uncertainty propagation methods for black-box-type problems journal May 2008
A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing journal June 2011
Design of computer experiments: A review journal November 2017
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index journal February 2010
An efficient methodology for modeling complex computer codes with Gaussian processes journal June 2008
Chaospy: An open source tool for designing methods of uncertainty quantification journal November 2015
Polynomial chaos expansion for sensitivity analysis journal July 2009
Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator journal March 2003

Figures / Tables (3)

Figure 1(p. 4)figure Figure 1
Figure 2(p. 5)figure Figure 2
Figure 3(p. 6)figure Figure 3

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Related Subjects

97 MATHEMATICS AND COMPUTING
Full Factorial Numerical Integration
Halton and Sobol series
Polynomial Chaos Expansion
Uncertainty Propagation Method
Univariate Dimension Reduction