@article{996c31ceca364334a981aa10a98b9527,
title = "A hybrid workflow for investigating wide DEM parameter spaces",
abstract = "Calibration of contact parameters for the DEM approach remains one of the critical obstacles for an accurate description of powder flows. Ideally, such a calibration approach relies on various macroscopic responses to identify an acceptable set of contact parameters. A significant challenge arises since the parameter space for models contains at least 10 degrees of freedom. The delicate task is to develop a framework that addresses the above-mentioned problems. In this paper, a flexible framework is presented that tackles these challenges by combining DEM simulations with regression methods. A surrogate model is trained, making it possible to identify parameter combinations in a fast and effective manner. The applicability was proven for a test powder, and multiple varieties of DEM parameters were determined. Due to the analytical structure of the surrogate model, it becomes computational feasibility to combine it with any optimization algorithm.",
keywords = "Artificial neural network, Bulk characterization, Calibration, DEM, Regression, Surrogate model",
author = "T. Forgber and Khinast, {J. G.} and E. Fink",
note = "Funding Information: The Research Center Pharmaceutical Engineering (RCPE) is funded within the framework of COMET - Competence Centers for Excellent Technologies by BMK, BMDW, Land Steiermark, and SFG. The COMET program is managed by the FFG. Funding Information: The minor contribution of k adh and μ s can be attributed to the formation of the initial packing configuration upon settling the randomly initialized particle bed. The same applies to the minor contribution of the Bond number to the initial packing. This can be explained by the selected ranges of Bo and the fact that the initial packing was generated such that no short-range particle interaction force was present upon initialization. Therefore, upon contact, the two above-mentioned parameters contribute to a greater extent to the initial particle bed configuration. The above-shown leading parameter importance is also supported by Orefice and Khinast [ 9 ]. Publisher Copyright: {\textcopyright} 2022",
year = "2022",
month = may,
doi = "10.1016/j.powtec.2022.117440",
language = "English",
volume = "404",
journal = "Powder Technology",
issn = "0032-5910",
publisher = "Elsevier B.V.",
}