Research Papers

Toward the Development of a Verification, Validation, and Uncertainty Quantification Framework for Granular and Multiphase Flows—Part 1: Screening Study and Sensitivity Analysis

[+] Author and Article Information
Aytekin Gel

National Energy Technology Laboratory (NETL),
Morgantown, WV 26507;
ALPEMI Consulting, L.L.C.,
Phoenix, AZ 85284
e-mail: aike@alpemi.com

Avinash Vaidheeswaran

National Energy Technology Laboratory (NETL),
Morgantown, WV 26507;
West Virginia University Research Corporation,
Morgantown, WV 26506
e-mail: avinash.vaidheeswaran@netl.doe.gov

Jordan Musser

National Energy Technology Laboratory (NETL),
Morgantown, WV 26507
e-mail: jordan.musser@netl.doe.gov

Charles H. Tong

Center for Applied Scientific Computing (CASC),
Lawrence Livermore National Laboratory (LLNL),
Livermore, CA 94550
e-mail: tong10@llnl.gov

1Corresponding author.

Manuscript received October 3, 2017; final manuscript received October 12, 2018; published online November 22, 2018. Assoc. Editor: Ashley F. Emery. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Verif. Valid. Uncert 3(3), 031001 (Nov 22, 2018) (12 pages) Paper No: VVUQ-17-1032; doi: 10.1115/1.4041745 History: Received October 03, 2017; Revised October 12, 2018

Establishing the credibility of computational fluid dynamics (CFD) models for multiphase flow applications is increasingly becoming a mainstream requirement. However, the established verification and validation (V&V) Standards have been primarily demonstrated for single phase flow applications. Studies to address their applicability for multiphase flows have been limited. Hence, their application may not be trivial and require a thorough investigation. We propose to adopt the ASME V&V 20 Standard and explore its applicability for multiphase flows through several extensions by introducing some of the best practices. In the current study, the proposed verification, validation, and uncertainty quantification (VVUQ) framework is presented and its preliminary application is demonstrated using the simulation of granular discharge through a conical hopper commonly employed in several industrial processes. As part of the proposed extensions to the V&V methodology, a detailed survey of subject matter experts including CFD modelers and experimentalists was conducted. The results from the survey highlighted the need for a more quantitative assessment of importance ranking in addition to a sensitivity study before embarking on simulation and experimental campaigns. Hence, a screening study followed by a global sensitivity was performed to identify the most influential parameters for the CFD simulation as the first phase of the process, which is presented in this paper. The results show that particle–particle coefficients of restitution and friction are the most important parameters for the granular discharge flow problem chosen for demonstration of the process. The identification of these parameters is important to determine their effect on the quantities of interest and improve the confidence level in numerical predictions.

Copyright © 2018 by American Society of Mechanical Engineers
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Fig. 6

Results of subject matter expert survey in identifying control variables (N/R implies no response)

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Fig. 7

Results of subject matter expert survey in identifying held-constant factors

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Fig. 5

Results of subject matter expert survey in identifying control variables

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Fig. 4

Results of subject matter expert survey in identifying response variables (quantities of interest)

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Fig. 2

Phase 2(b) VVUQ flow chart

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Fig. 1

Verification, validation, and uncertainty quantification flow chart phases 1 and 2 (a)

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Fig. 8

Scatter plot for screening study

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Fig. 9

Method of Morris-based analysis results from the screening study simulations as number of samples increased from 44 to 110: (a) using the first 44 samples (N = 44), (b) using the first 55 samples (N = 55), (c) using the first 77 samples (N = 77), and (d) using all 110 samples (N = 110)

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Fig. 10

Results of OLH sampling based simulations: (a) OLH with n = 40 samples and (b) OLH with n = 80 samples

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Fig. 11

Contour plot of the surrogate model constructed from 40 samples for the discharge flow rate shown as a function of particle–particle coefficient of friction and particle–particle coefficient of restitution, where the remaining input parameters are set at their nominal values

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Fig. 12

Cross-validation error assessment plots: (a) histogram of errors and (b) comparison of predictions from the surrogate model and actual MFIX-DEM results with 40 samples

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Fig. 13

Global sensitivity results based on n = 40 samples for first quantity of interest, discharge rate: (a) Sobol' main effect sensitivities, (b) Sobol' total indices, and (c) Sobol' two-way sensitivities for interaction of input parameters

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Fig. 14

Global sensitivity results based on n = 40 samples for second quantity of interest, angle of repose: (a) Sobol' main effect sensitivities, (b) Sobol' total indices, and (c) Sobol' two-way sensitivities for interaction of input parameters



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