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Research Papers

Multiscale Validation and Uncertainty Quantification for Problems With Sparse Data

[+] Author and Article Information
Anchal Jatale

Institute for Clean and Secure Energy,
Department of Chemical Engineering,
University of Utah,
155 S 1452 E #350,
Salt Lake City, UT 84112
e-mail: anchal.jatale@gmail.com

Philip J. Smith

Institute for Clean and Secure Energy,
Department of Chemical Engineering,
University of Utah,
155 S 1452 E #350,
Salt Lake City, UT 84112
e-mail: philip.smith@utah.edu

Jeremy N. Thornock

Institute for Clean and Secure Energy,
Department of Chemical Engineering,
University of Utah,
155 S 1452 E #350,
Salt Lake City, UT 84112
e-mail: j.thornock@utah.edu

Sean T. Smith

Institute for Clean and Secure Energy,
Department of Chemical Engineering,
University of Utah,
155 S 1452 E #350,
Salt Lake City, UT 84112
e-mail: sean.t.smith@utah.edu

Jennifer P. Spinti

Institute for Clean and Secure Energy,
Department of Chemical Engineering,
University of Utah,
155 S 1452 E #350,
Salt Lake City, UT 84112
e-mail: jennifer.spinti@utah.edu

Michal Hradisky

Institute for Clean and Secure Energy,
Department of Chemical Engineering,
University of Utah,
155 S 1452 E #350,
Salt Lake City, UT 84112
e-mail: michal.hradisky@gmail.com

1Corresponding author.

Manuscript received August 2, 2016; final manuscript received January 26, 2017; published online February 9, 2017. Assoc. Editor: Jeffrey E. Bischoff.

J. Verif. Valid. Uncert 2(1), 011001 (Feb 09, 2017) (10 pages) Paper No: VVUQ-16-1022; doi: 10.1115/1.4035864 History: Received August 02, 2016; Revised January 26, 2017

Quantification of uncertainty in the simulation results becomes difficult for complex real-world systems with little or no experimental data. This paper describes a validation and uncertainty quantification (VUQ) approach that integrates computational and experimental data through a range of experimental scales and a hierarchy of complexity levels. This global approach links dissimilar experimental datasets at different scales, in a hierarchy, to reduce quantified error bars on case with sparse data, without running additional experiments. This approach was demonstrated by applying on a real-world problem, greenhouse gas (GHG) emissions from wind tunnel flares. The two-tier validation hierarchy links, a buoyancy-driven helium plume and a wind tunnel flare, to increase the confidence in the estimation of GHG emissions from wind tunnel flares from simulations.

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Figures

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Fig. 1

VUQ hierarchy for a real-world problem

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Fig. 2

Intralevel and interlevel linkage in a hierarchy

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Fig. 3

VUQ hierarchy for determining GHG emissions from industrial flares

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Fig. 4

Schematic of the FLAME facility at Sandia National Laboratories

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Fig. 5

Schematic of FTF. Adapted from Ref. [17].

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Fig. 6

Consistent region for helium case in Sct and helium inlet velocity space: (a) component-scale VUQ analysis [10] and (b) global VUQ analysis

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Fig. 7

Consistent region for helium case in Sct and air coflow space: (a) component-scale VUQ analysis [10] and (b) global VUQ analysis

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Fig. 8

Error bars for 195 points at three different heights (left = 0.2 m, middle = 0.4 m, and right = 0.6 m) to show the consistent space for the helium plume case: (a) component-scale VUQ [10] and (b) global VUQ analysis

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Fig. 9

Prior and posterior consistent regions for CO2 concentration in all six crosswind groups: (a) pilot-scale analysis [18] and (b) global VUQ analysis

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Fig. 10

Prior and posterior consistent regions for O2 concentration in all six crosswind groups: (a) pilot-scale analysis [18] and (b) global VUQ analysis

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Fig. 11

Prior and posterior consistent regions for CH4 concentration in all six crosswind groups: (a) pilot-scale analysis [18] and (b) global VUQ analysis

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Fig. 12

Consistency region for two crosswind groups in α space for pilot-scale and global VUQ analysis: (a) [3.373–3.932] and (b) [8.928–10.169] m/s

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Fig. 13

Consistency region for two crosswind groups in Sct space for pilot-scale and global VUQ analysis: (a) [3.373–3.932] and (b) [8.928–10.169] m/s

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