Coupled three dimensional DEM–CFD simulation of a lime shaft kiln—Calcination, particle movement and gas phase flow field

Krause, Bastian; Liedmann, B.; Wiese, Jens; Wirtz, S.; Scherer, Viktor

doi:10.1016/j.ces.2015.06.002

Cited by 38 publications

(23 citation statements)

References 28 publications

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“…(13) and (14). These snapshots capture the full spatial resolution of the temperature and moisture field of (13) and (14), but, as the term snapshot suggests, they are constant in time (see Fig. 5 for examples for the heat transfer).…”

Section: Model Reduction In a Nutshellmentioning

confidence: 98%

“…More precisely, the snapshots are the most important eigenmodes of the spatio-temporal behavior described by the PDEs (13) and (14). In fact, it is more appropriate to use the term ''mode", because ''snapshot" has a different meaning in the model reduction literature.…”

Section: Model Reduction In a Nutshellmentioning

confidence: 99%

“…A Discrete Element code developed at the Department of Energy Plant Technology, Ruhr-University Bochum computes the motion of the particulate phase [14]. In the DEM, first introduced by Cundall and Strack [15], translational and rotational movement of each particle are calculated by solving Newton's and Euler's laws of motion.…”

Section: Particle Motionmentioning

confidence: 99%

“…The proposed model reduction approach is applied to a finite element simulation of the heat and mass transfer equations (13), (14) with boundary conditions as explained in Section 3 on a grid with N ¼ 736 points and a total simulation time of 600 s. The moisture and temperature distributions are recorded every 5 s, which implies M ¼ 120. For simplicity we determine and discuss a ROM for the temperature only and use the original finite volume simulation data for the moisture in all calculations.…”

Section: Reduced Order Model Studymentioning

confidence: 99%

“…We determined a reduced order model for the energy balance (13) and used the original Finite Volume simulation data for the mass transport modeled by (14) to account for the coupling of heat conduction and evaporation. For example, the material parameters k eff ðXÞ; c p ðXÞ and qðXÞ appear in the energy balance (13) and depend on the moisture X that is governed by the mass balance (14).…”

Section: Reduced Order Model Studymentioning

confidence: 99%

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Coupled DEM–CFD simulation of drying wood chips in a rotary drum – Baffle design and model reduction

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Section: Model Reduction In a Nutshellmentioning

confidence: 98%

Section: Model Reduction In a Nutshellmentioning

confidence: 99%

Section: Particle Motionmentioning

confidence: 99%

Section: Reduced Order Model Studymentioning

confidence: 99%

Section: Reduced Order Model Studymentioning

confidence: 99%

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Coupled DEM–CFD simulation of drying wood chips in a rotary drum – Baffle design and model reduction

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Simulation of Reacting, Moving Granular Assemblies of Thermally Thick Particles by Discrete Element Method/Computational Fluid Dynamics

et al. 2023

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The potential and limitations of the discrete element method (DEM) coupled with computational fluid mechanics (CFD) to simulate chemically reacting, moving granular material interacting with a fluid flow are summarized. A special focus is set on thermally thick particles, which requires to resolve the intraparticle transport and reaction processes. The aspect of complex particle shape is addressed, as shape may dominate the particle behavior in densely packed granular assemblies even more than the details of contact force laws. The fluid flow in the granular assembly is assumed to be a gas. The potential of DEM/CFD will be highlighted presenting three illustrative examples: a large-scale lime shaft kiln with intermittent operation, an industrial-size grate firing system for the incineration of municipal waste, and a small-scale straw pellet stove.

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Toward Data‐Assisted Particle‐Fluid Simulations of Heat Transfer in Blast Furnaces

Lichtenegger

Pirker

2020

steel research int.

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Detailed particle‐fluid simulations of moving bed reactors such as blast furnaces (BFs) are computationally very expensive. The wide range of temporal scales from short‐lived micromechanics to slow, macroscopic transport makes it almost impossible to study e.g., heat transfer over process‐relevant time scales. However, a recently introduced approach that uses high‐fidelity data obtained with the discrete element method (DEM) enables extremely fast data‐assisted simulations. Using this methodology, calculations are conducted, which are efficient enough to study granular motion through a BF slot model subject to heat transfer between the hot blast and the solid phase over many hours. These long durations make it possible to determine the thermal steady state including shape and location of the cohesive zone (CZ). While other approaches either have to predefine its properties or are bound to very small domain sizes, this novel strategy is applied to a large‐scale BF slot model with about 28 m height and 15 m width with hardly any prior knowledge. It constitutes an important step toward virtual experiments on BFs under realistic conditions and will allow researchers to gain new insights, perform variations of operating parameters, and ultimately build an online monitoring system.

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Coupled three dimensional DEM–CFD simulation of a lime shaft kiln—Calcination, particle movement and gas phase flow field

Cited by 38 publications

References 28 publications

Coupled DEM–CFD simulation of drying wood chips in a rotary drum – Baffle design and model reduction

Coupled DEM–CFD simulation of drying wood chips in a rotary drum – Baffle design and model reduction

Simulation of Reacting, Moving Granular Assemblies of Thermally Thick Particles by Discrete Element Method/Computational Fluid Dynamics

Toward Data‐Assisted Particle‐Fluid Simulations of Heat Transfer in Blast Furnaces

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