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ArticleName Post-impact recovery coefficient calibration in DEM modeling of granular materials
DOI 10.17580/or.2020.04.07
ArticleAuthor Vasilyeva N. V., Erokhina O. O.

Saint-Petersburg Mining University (Saint-Petersburg, Russia):
Vasilyeva N. V., Associate Professor, Candidate of Engineering Sciences,
Erokhina O. O., Student,


Discrete element modeling (DEM) is associated with a number of difficulties, one of which is the calibration of DEM parameters for granular materials. The values of these parameters are critical for the accuracy of the resulting model. Ores, on the other hand, may have different grain-size compositions with varying particle shapes and sizes. Establishing the appropriate values of the recovery coefficient for ores is, therefore, quite a labor-intensive task. The article provides an overview of the existing methods for measuring the recovery coefficients. The study presents new original methods for establishing the coefficient of recovery for particle-to-particle (CoRPP) and particle-to-surface (CoRPB) interactions. These are based on visual imaging of the behavior of a particle system using a high-speed camera and converting the data collected into specific coefficient values. The methods proposed have been tested in simulations based on the discrete element modeling method using experimental stands for various particle shapes and sizes. A procedure has been developed for determining CoRPB values with sufficient accuracy. A test stand has been manufactured for its specific implementation. The results of the study demonstrate the need for developing an indirect CoRPP measurement methodology. Moreover, part of the methods developed for the CoRPP may be applied with due account of the restrictions imposed. These methods may be applicable both in the mining industry and in metallurgy, pharmaceuticals, and the food industry, and may be used to design a universal stand for determining all DEM modeling parameters.

keywords Discrete element method, calibration, granular materials, recovery coefficient, ore, mining industry, modeling

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