Doctoral Thesis / Dissertation from the year 2013 in the subject Engineering - Mechanical Engineering, grade: 1, University of Linz (Department on Particulate Flow Modelling), language: English, abstract: The numerical hybrid model EUgran+, 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 PHD 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.

This study presents the basic models for discrete and continuous particle laden flow simulation. An overview of the two main approaches, the Lagrangian discrete particle model and the Eulerian granular phase model is given. Moreover these two approaches are combined to a hybrid model to use the benefits of the discrete and continuous description. This safes computational time and increase the efficiency of particle laden flow simulations. Furthermore the models are extended to poly-disperse particles including a simple agglomeration model based on a population balance equation. Finally the usability of the models is shown at a pneumatic particle transport system including particle strand building and the separation of particles using an industrial cyclone.

Modelling Approaches and Computational Methods for Particle-laden Turbulent Flows introduces the principal phenomena observed in applications where turbulence in particle-laden flow is encountered while also analyzing the main methods for analyzing numerically. The book takes a practical approach, providing advice on how to select and apply the correct model or tool by drawing on the latest research. Sections provide scales of particle-laden turbulence and the principal analytical frameworks and computational approaches used to simulate particles in turbulent flow. Each chapter opens with a section on fundamental concepts and theory before describing the applications of the modelling approach or numerical method. Featuring explanations of key concepts, definitions, and fundamental physics and equations, as well as recent research advances and detailed simulation methods, this book is the ideal starting point for students new to this subject, as well as an essential reference for experienced researchers. - Provides a comprehensive introduction to the phenomena of particle laden turbulent flow - Explains a wide range of numerical methods, including Eulerian-Eulerian, Eulerian-Lagrange, and volume-filtered computation - Describes a wide range of innovative applications of these models

This book has been conceived to provide guidance on the theory and design of cyclone systems. Forthose new to the topic, a cyclone is, in its most basic form, a stationary mechanical device that utilizes centrifugal force to separate solid or liquid particles from a carrier gas. Gas enters near the top via a tangential or vaned inlet, which gives rise to an axially descending spiral of gas and a centrifugal force field that causes the incoming particles to concentrate along, and spiral down, the inner walls of the separator. The thus-segregated particulate phase is allowed to exit out an underflow pipe while the gas phase constricts, and - in most separators - reverses its axial direction of flow and exits out a separate overflow pipe. Cyclones are applied in both heavy and light industrial applications and may be designed as either classifiers or separators. Their applications are as plentiful as they are varied. Examples include their use in the separation or classification of powder coatings, plastic fines, sawdust, wood chips, sand, sintered/powdered meta!, plastic and meta! pellets, rock and mineral cmshings, carbon fines, grain products, pulverized coal, chalk, coal and coal ash, catalyst and petroleum coke fines, mist entrained off of various processing units and liquid components from scmbbing and drilling operations. They have even been applied to separate foam into its component gas and liquid phases in recent years.

This dissertation covers four topics relevant for particle-laden flow simulation and modeling. First we perform verification of three techniques for mesoscale simulation of particle-laden flows: the volume filtered Euler-Lagrange method, the two fluid model, and the anisotropic Gaussian method. We verify that statistics from Euler-Lagrange simulations of cluster-induced turbulence (CIT) can be extracted with weak parameter sensitivity. These statistics were also found to match well with CIT simulations using the anisotropic Gaussian method. Next, we use the volume filtered Euler-Lagrange method to perform simulations of homogeneous shear cluster-induced turbulence (HSCIT). We find the evolution of HSCIT to be strongly dependent on the relative orientation of shear and gravity. Third, we provide a 3-D extension to the conditional hyperbolic method of moments (CHyQMOM) for mesoscale particle-laden flows. CHyQMOM is a high order, hyperbolic closure to the kinetic moment equations. Lastly, we introduce a regression technique combining Fourier pseudospectral numerical methods and neural networks for finding nonlinear spatial operators from data.