Hybrid Particle Laden Flow Modelling - A joint domain combination of Eulerian solid phase and Lagrangian discrete particle simulations

David Schellander

Research output: ThesisDoctoral thesis

Abstract

The numerical hybrid model EUgran+ [Pirker et al., 2010], which is an Eulerian-Eulerian granular phase model extended with models from the Eulerian-Lagrangian model for dense rapid particulate flows, is modified to account for poly-dispersed particle diameter distributions. These modifications include the implementation of I) a new poly-dispersed drag law and of II) new particle boundary conditions distinguishing between sliding and non-sliding particle-wall collisions and III) a new implementation of the population balance equation in the agglomeration model using the Eulerian-Lagrangian approach, referred to as Bus-stop model. Further, the applicability of the EUgran+ model is extended to cover dilute to dense poly-disperse particulate flows. Furthermore, this provides an improvement in the numerical simulation of dust separation and the formation of particle strands in industrial scale cyclones. In this thesis, the EUgran+Poly model is validated at 3 specific cases with different mass loadings: I) poly-dispersed particle conveying in a square pipe with a 90 degree bend at low mass loading (L = 0:00206); II) a particle conveying case in a rectangular pipe with a double-loop at high mass loading (L = 1:5); III) in a vertical pipe the implementation of the agglomeration model is validated. To show the applicability of the presented models a simulation of an industrial cyclone in experimental scale is presented. The validation and application shows that considering a poly-disperse Eulerian-Eulerian granular phase improves the accordance of the simulation results with measurements significantly. Finally, the hybrid model is a good compromise for a computational efficient simulation of particulate transport and separation with different mass loading regimes.
Original languageEnglish
Publication statusPublished - Mar 2013

Fields of science

  • 103032 Fluid mechanics
  • 203 Mechanical Engineering
  • 203016 Measurement engineering
  • 203024 Thermodynamics
  • 211104 Metallurgy
  • 204006 Mechanical process engineering
  • 204007 Thermal process engineering
  • 103043 Computational physics

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing
  • Engineering and Natural Sciences (in general)

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