EPJ Web Conf.
Volume 249, 2021Powders & Grains 2021 – 9th International Conference on Micromechanics on Granular Media
|Number of page(s)||4|
|Published online||07 June 2021|
From discrete element simulation data to process insights
Department of Mechanical and Aeronautical Engineering, University of Pretoria, South Africa
2 CSIRO Data61, Private Bag 10, Clayton, South Victoria, 3169, Australia
3 Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13, 8010 Graz, Austria
4 Univ. Lille, Institut Mines-Télécom, Univ. Artois, Junia, ULR 4515 LGCgE Laboratoire de Génie Civil et géoEnvironnement, F-59000 Lille, France
* e-mail: firstname.lastname@example.org
Published online: 7 June 2021
Industrial-scale discrete element simulations typically generate Gigabytes of data per time step, which implies that even opening a single file may require 5 - 15 minutes on conventional magnetic storage devices. Data science’s inherent multi-disciplinary nature makes the extraction of useful information challenging, often leading to undiscovered details or new insights. This study explores the potential of statistical learning to identify potential regions of interest for large scale discrete element simulations. We demonstrate that our in-house knowledge discovery and data mining system (KDS) can decompose large datasets into i) regions of potential interest to the analyst, ii) multiple decompositions that highlight different aspects of the data, iii) simplify interpretation of DEM generated data by focusing attention on the interpretation of automatically decomposed regions, and iv) streamline the analysis of raw DEM data by letting the analyst control the number of decomposition and the way the decompositions are performed. Multiple decompositions can be automated in parallel and compressed, enabling agile engagement with the analyst’s processed data. This study focuses on spatial and not temporal inferences.
A video is available at https://doi.org/10.48448/qs7x-7e87
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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