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Synthetic Packed-Bed Generation for CFD Simulations: Blender vs. STAR-CCM+

1
Institute of Chemical and Electrochemical Process Engineering, Clausthal University of Technology, Leibnizstr. 17, 38678 Clausthal-Zellerfeld, Germany
2
Research Center Energy Storage Technologies (EST), Clausthal University of Technology, Am Stollen 19A, 38640 Goslar, Germany
*
Author to whom correspondence should be addressed.
ChemEngineering 2019, 3(2), 52; https://doi.org/10.3390/chemengineering3020052
Received: 8 April 2019 / Revised: 3 May 2019 / Accepted: 7 May 2019 / Published: 14 May 2019
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Abstract

A common reactor type in the chemical and process industry is the fixed-bed reactor. Accurate modeling can be achieved with particle-resolved computational fluid dynamic (CFD) simulations. However, the underlying bed morphology plays a paramount role. Synthetic bed-generation methods are much more flexible and faster than image-based approaches. In this study, we look critically at the two different bed generation methods: Discrete element method (DEM) (in the commercial software STAR-CCM+) and the rigid-body model (in the open-source software Blender). The two approaches are compared in terms of synthetically generated beds with experimental data of overall and radial porosity, particle orientation, as well as radial velocities. Both models show accurate agreement for the porosity. However, only Blender shows similar particle orientation than the experimental results. The main drawback of the DEM is the long calculation time and the shape approximation with composite particles. View Full-Text
Keywords: fixed-bed reactor; blender; discrete element method; CFD fixed-bed reactor; blender; discrete element method; CFD
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Flaischlen, S.; Wehinger, G.D. Synthetic Packed-Bed Generation for CFD Simulations: Blender vs. STAR-CCM+. ChemEngineering 2019, 3, 52.

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