Article révisé par les pairs
Résumé : There is a high demand for accurate and fast numerical models for dense granular flows found in many industrial applications. Nevertheless, before numerical model can be used its need to be always validated against experimental data. During the validation, it is important to consider how the measurement data sets, as well as the numerical models, are affected by errors and uncertainties. In this study, the uncertainty quantification for the Discrete Element Method (DEM) model was performed based on selected quantities of interest (QOI), which were measured at a test rig. The uncertainty quantification was performed with open-source Dakota code, and the Latin hypercube sampling technique was used to determine test points. Various correlations between the input and output data were investigated to assess the impact of the possible input data errors on the output values determined by the solver. The results were validated against the measurement data from a novel in-house experimental test rig. The novel character of this work is developed procedure for study the impact of the uncertainties related with the input data on numerical results delivered by DEM model. In-house algorithm written using OpenCV libraries for determining particle motion characteristics from image dataset was used for results determination and postprocessing.

Documents en relation

DI-fusion