Title: LES of Particle-Laden Flow in Sharp Pipe Bends with Data-Driven Predictions of Agglomerate Breakage by Wall Impacts
Authors: Khalifa, Ali  
Gollwitzer, Jasper
Breuer, Michael  
Language: eng
Keywords: LES;Particle-laden flows;Agglomerate breakage;Wall impact;Data-driven modeling;Artificial neural network
Subject (DDC): DDC - Dewey Decimal Classification::000 Informatik, Wissen, Systeme
DDC - Dewey Decimal Classification::500 Naturwissenschaften
DDC - Dewey Decimal Classification::600 Technik
Issue Date: 25-Nov-2021
Publisher: MDPI
Document Type: Article
Source: Fluids 2021, 6, 424
Journal / Series / Working Paper (HSU): Fluids 
Volume: 6
Issue: 12
Pages: 26 S.
Publisher Place: Basel ; Beijing ; Wuhan ; Barcelona ; Belgrade
The breakage of agglomerates due to wall impact within a turbulent two-phase flow is
studied based on a recently developed model which relies on two artificial neural networks (ANNs).
The breakup model is intended for the application within an Euler-Lagrange approach using the
point-particle assumption. The ANNs were trained based on comprehensive DEM simulations. In the
present study the entire simulation methodology is applied to the flow through two sharp pipe bends
considering two different Reynolds numbers. In a first step, the flow structures of the continuous flow
arising in both bend configurations are analyzed in detail. In a second step, the breakage behavior of
agglomerates consisting of spherical, dry and cohesive silica particles is predicted based on the newly
established simulation methodology taking agglomeration, fluid-induced breakage and breakage
due to wall impact into account. The latter is found to be the dominant mechanism determining the
resulting size distribution at the bend outlet. Since the setups are generic geometries found in dry
powder inhalers, important knowledge concerning the effect of the Reynolds number as well as the
design type (one-step vs. two-step deflection) can be gained.
Organization Units (connected with the publication): Strömungsmechanik 
Publisher DOI: 10.3390/fluids6120424
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