Toward reducing uncertainty quantification costs in DEM models of particulate flow: Testing simple, sensitivity-based, forward uncertainty propagation techniques

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.

Automated summary

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.