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Outline

Title
Abstract
Project Overview
The Experimental Unit and Instrumentation
Experimental Method
Experimental Results
Simulation Results of Bubbling Bed
Simulation Results of Turbulent Bed
Discussion on the Gas-Solid Drag Force
Simulation Results
Theoretical Background
Data Analysis
Inverse Problem: Stationary Case
Computational Calibration Method
Experimental Set-Up
Benchmarking Results
Sensitivity Method
Modified Newton-Raphson Method
Prototype System Design
Summary
Piv-Methods
Optical Measurement Methods for Multiphase Flows
Overlapping Object Separation Methods
Performance of the Methods
Data Handling
Velocity Field Post-Processing Methods
Detection and Analysis of Coherent Flow Structures
Methods
Results and Applications
Introduction
Experimental Studies at VTT
Background
Method
Results
Experimental Procedure
Conclusions
References
All Topics
Engineering
Chemical Engineering

Multiphase flows in process industry: ProMoni

Profile image of Juha SalmelaJuha Salmela

2005

Abstract

The project "Multiphase flows in process industry (ProMoni)" 1.1.2001– 30.4.2004 was a research consortium carried out jointly by seven research groups from VTT, University of Jyväskylä, Tampere University of Technology, Åbo Akademi University and University of Kuopio. It included modeling, development and validation of numerical methods as well as development of new experimental techniques for multiphase flows found in process industry. The primary fields of application are in fluidized beds and in various processes found in the paper and pulp industry. This extensive final report of the ProMoni project includes results from experimental and numerical research of bubbling fluidized beds indicating that modelling produces the bed behaviour realistically and the simulations can be used in developing approximative macroscopic two-phase models. A new gas-solid drag correlation model was successfully used within Fluent CFD code to simulate the typical flow structures in a circ...

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References (123)

  1. Non-stationary inverse problems ..........................................59
  2. 4.1.4 Camera tomography..............................................................59
  3. 4.2 Methods................................................................................................59 3.4.2.1 Forward problem...................................................................59
  4. 4.2.2 Inverse problem: stationary case...........................................60
  5. 4.2.3 Computational calibration method........................................60
  6. 4.2.4 Non-stationary inverse problems ..........................................61
  7. 4.2.5 Camera-tomography..............................................................61
  8. 4.3 Results ..................................................................................................61 3.4.3.1 Forward problem...................................................................61
  9. 4.3.2 Inverse problem.....................................................................64
  10. 4.3.3 Computational calibration method........................................65
  11. 4.3.4 Non-stationary inverse problems: state estimation approach ..... 67
  12. Camera-tomography..............................................................69
  13. 4.4 Experimental set-up .............................................................................71
  14. 4.5 Benchmarking results...........................................................................71
  15. 4.6 Conclusions ..........................................................................................77
  16. 4.7 References ............................................................................................78
  17. 5 Impedance Tomography...................................................................................80 3.5.1 Background ..........................................................................................80 3.5.2 Physical model of impedance tomography ..........................................81
  18. 5.3 Image reconstruction algorithm ...........................................................82 3.5.4 Prototype system design.......................................................................88 3.5.5 Monitoring consistency in pulp flow ...................................................89
  19. 5.6 Monitoring air bubbles in pulp flow ....................................................91
  20. 5.7 Measuring velocity in pulp flow ..........................................................93
  21. 5.8 Summary ..............................................................................................95 3.5.
  22. 9 References ............................................................................................95
  23. 6 PIV-methods.....................................................................................................97 3.6.1 Background ..........................................................................................97 3.6.2 Optical measurement methods for multiphase flows ...........................98
  24. 6.3 Overlapping object separation methods .............................................105 3.6.4 Estimation of the fiber orientation .....................................................107
  25. 6.5 Some applications ..............................................................................111 3.6.5.1 Turbulent bubbly flow measurements in a mixing vessel with PIV ......................................................................................111
  26. 6.5.2 Turbulent bubbly flow in the outlet pipe of a centrifugal pump....................................................................................113
  27. 6.5.3 Characterization of turbulent flow and floc morphology in a flocculation process: PIV/Digital imaging experiments .....113
  28. 6.6 Velocity field post-processing methods .............................................115
  29. 4 Current research profile of the participating groups. Impacts of the ProMoni project ....170
  30. 5 Conclusions..............................................................................................................176
  31. Appendix A: Project organization 3.1.10 References
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The hydrodynamic behavior of gas-solid flow is investigated in a 2-D bubbling fluidized bed reactor filled with 530 mm particles. The Computational Fluid Dynamics (CFD) is used to simulate the complex transient behavior of gas-solid flow. The CFD simulation of the bed hydrodynamics is based on the concept of Euler-Euler two-fluid model in combination with Kinetic Theory of Granular Flow (KTGF). In the present study, four different drag models are used to determine the drag force between the two phases and the results are compared. It is observed that the drag model has a significant effect on the simulation of gas-solid flow. The Gidaspow drag model and Syamlal-O'Brien model could predict the core-annulus structure of the bed very well. In comparison, the energy minimization multi-scale (EMMS) model and McKeen model cannot clearly predict the core-annulus structure of the flow. The Gisadpow model was found to provide better agreement with the experimental results of timeaveraged particle velocity. On the other hand, the Syamlal-O'Brien model and EMMS model predicted the time-averaged granular temperature comparatively well. The effect of turbulence modeling on the flow behavior is also studied by incorporating the RNG k-e turbulence model. The results showed that the effect of turbulence modeling on the bed hydrodynamics is not very significant.

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Related topics

  • Materials Science
  • Multiphase Flow
  • Process industry
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  • Fluidized Beds
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