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CFD-DEM Fluidized Bed Drying Study Using a Coarse-Graining Technique.

M J A de Munck1, E A J F Peters1, J A M Kuipers1

  • 1Multiphase Reactors Group, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands.

Industrial & Engineering Chemistry Research
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

Coarse-grained computational fluid dynamics-discrete element method (CFD-DEM) modeling enables larger fluidized bed simulations for industrial drying. This approach accurately captures particle temperature, density, and drying performance (Sherwood number).

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Area of Science:

  • Chemical Engineering
  • Particle Technology
  • Computational Science

Background:

  • Fluidized beds are widely used in industrial drying processes.
  • Computational fluid dynamics-discrete element method (CFD-DEM) enhances understanding of solids drying but faces computational limitations for large systems.

Purpose of the Study:

  • To investigate the application of coarse-grained CFD-DEM scaling laws for simulating fluidized bed solids drying.
  • To assess the computational efficiency and accuracy of coarse-graining for larger fluidized bed systems.

Main Methods:

  • Implementation of coarse-graining CFD-DEM scaling laws.
  • Simulation of fluidized beds under three different superficial gas velocities.
  • Analysis of particle temperature, density, and Sherwood number.

Main Results:

  • Accurate description of particle temperature and density was achieved.
  • The Sherwood number, a key indicator of drying performance, was well-captured by the coarse-graining simulations.
  • Coarse-graining significantly reduces computational cost, enabling larger system simulations.

Conclusions:

  • Coarse-grained CFD-DEM is a viable and accurate method for simulating industrial fluidized bed drying.
  • This approach overcomes computational limitations, allowing for the study of larger and more complex drying systems.
  • The method provides reliable predictions of drying performance metrics like the Sherwood number.