LES of Particle-Laden Flow in Sharp Pipe Bends with Data-Driven Predictions of Agglomerate Breakage by Wall Impacts

Abstract

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.

Physics-informed data-driven unsteady Reynolds-averaged Navier–Stokes turbulence modeling for particle-laden jet flows

“…30 The incorporation of the GEP model into their LES was found to enhance the prediction of particle acceleration, velocity and clustering, while also yielding results close to their DNS. Khalifa et al 31 employed data-driven LES for particle-laden flows in pipe bend configurations to study the breakage of particle agglomerates due to wall impact. Artificial NN (ANN) were used to train the regression relationship between the fragmentation ratio and the fragment size parameters based on their comprehensive DEM database.…”