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

• Published in: Powder technology 2022-01, Vol.398 (C), p.117136, Article 117136
• Main Authors: Dahl, Steven R., LaMarche, W. Casey Q., Liu, Peiyuan, Fullmer, William D., Hrenya, Christine M.
• Format: Article
• Language: English
• Subjects: CFD-DEM; Computer applications; DEM; Flow; Nested loops; Propagation; Sampling; Sampling methods; Sensitivity analysis; Uncertainty; Uncertainty quantification; VV&UQ
• ISSN: 0032-5910; 1873-328X
• DOI: 10.1016/j.powtec.2022.117136

The performance of two conceptually-simple uncertainty quantification techniques are tested against the rigorous 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-sampling, 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 selection 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% (boundary sampling). [Display omitted] •Simple forward uncertainty propagation techniques are tested using two DEM models.•Very small-scale DEM models allow benchmarking against rigorous nested-loop technique.•Simplified UQ techniques perform well and reduce computational cost significantly.