期刊
POWDER TECHNOLOGY
卷 398, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.powtec.2022.117136
关键词
Uncertainty quantification; DEM; CFD-DEM; VV& UQ
资金
- U.S. Department of Energy [DE-FE0026298]
- National Science Foundation [ACI-1532235, ACI1532236]
- University of Colorado Boulder
- Colorado State University
The performance of two conceptually simple uncertainty quantification techniques are compared with the rigorous nested-loop sampling technique. The results demonstrate that these simplified techniques have significant computational advantages and yield uncertainties in model outputs that are consistent with the full-sampling method.
The performance of two conceptually-simple uncertainty quantification techniques are tested against the rigor-ous nested-loop sampling technique of Roy and Oberkampf (Comput Methods Appl Mech Eng, 200: 2131-2144, 2011) (herein called full-sampling) using two very small-scale DEM-based models of particulate flow (one gas -solid flow and one granular flow). The first simplified forward uncertainty propagation technique, reduced-sam-pling, uses a sensitivity analysis to eliminate uncertain inputs that have little impact on the model output prior to nested-loop sampling. The second technique, boundary-sampling, uses a sensitivity analysis to inform the selec-tion of two bounding cases for each key model output. The uncertainties in the model outputs obtained via the reduced-and boundary-sampling methods agree well with those from full-sampling for both the gas-solid and granular flow models while yielding computational savings of 65-75% (reduced sampling) and 94-97% (bound-ary sampling).(c) 2022 Elsevier B.V. All rights reserved.
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