Data set generation at novel test-rig for validation of numerical models for modeling granular flows
•A novel experimental data set for numerical model validation.•Experimental data uncertainty analysis.•Wide range of the experimental data for validation of the mathematical model dedicated for modelling particulate flows.•Validation of DEM approach against experimental data. [Display omitted] Signi...
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Published in | International journal of multiphase flow Vol. 142; p. 103696 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.09.2021
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Subjects | |
Online Access | Get full text |
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Summary: | •A novel experimental data set for numerical model validation.•Experimental data uncertainty analysis.•Wide range of the experimental data for validation of the mathematical model dedicated for modelling particulate flows.•Validation of DEM approach against experimental data.
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Significant effort has been exerted on developing fast and reliable numerical models for modeling particulate flow; this is challenging owing to the complexity of such flows. To achieve this, reliable and high-quality experimental data are required for model development and validation. This study presents the design of a novel test-rig that allows the visualization and measurement of particle flow patterns during the collision of two particle streams. Valuable data sets are provided for the validation of numerical models dedicated to granular flows. The experimental work was conducted for three particle distributions and different configurations of the test rig setup. Additionally, a standard discrete element method for modeling particle transport was applied to the test-rig configuration and the effects of the material spring constant on the predicated flow patterns are investigated. An additional purpose of these simulations was also to collect necessary data for further collision model validation, developed based on a reduced-order technique. |
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ISSN: | 0301-9322 1879-3533 |
DOI: | 10.1016/j.ijmultiphaseflow.2021.103696 |