Influence of grid resolution, parcel size and drag models on bubbling fluidized bed simulation

Lu, Liqiang; Konan, Arthur; Benyahia, Sofiane

doi:10.1016/j.cej.2017.06.002

Cited by 76 publications

(29 citation statements)

References 42 publications

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“…9,10 This approach is straightforward but expensive and unavailable to most engineers. 18 Although the size of bubbles and clusters can be estimated from empirical correlations or the energy minimization multiscale (EMMS) model, [19][20][21] these parameters vary greatly. This coarse CFD-DEM has been used to simulate chemical reactions, 13 heat transfer, 14 and hydrodynamics 15,16 of fluidized bed and cyclone separators.…”

Section: Introductionmentioning

confidence: 99%

“…In this research, we refer to this number, which is greater than one, as the statistic weight and represent it with the symbol W. Statistic weight has a considerable influence on both the simulation speed and the accuracy of its numerical results. The EMMS drag model is effective for coarse CFD-DEM simulations of bubbling fluidized bed 18 and riser, 21,34 and the filtered drag model has been validated for coarse CFD-DEM simulation of CFB risers. As W increases, the speed of the simulation will increase drastically while its accuracy diminishes.…”

Section: Introductionmentioning

confidence: 99%

“…Only qualitative criteria were mentioned for systems with distinct meso-scale structures like bubbles and clusters, where the parcel size should be small enough to resolve the deformation, aggregation, and breakage of bubbles or clusters. 18 Although the size of bubbles and clusters can be estimated from empirical correlations or the energy minimization multiscale (EMMS) model, [19][20][21] these parameters vary greatly. To quantify the uncertainties due to particle coarsening, we simply compare these simulation results with those obtained with the most accurate discrete particle simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, sub-grid drag corrections are used in these coarse-grid simulations, such as the EMMS drag models 19,24-28 based on energy minimization multi-scale method, 29 or the filtered drag models [30][31][32][33] derived from fine-grid TFM or CFD-DEM simulations. The EMMS drag model is effective for coarse CFD-DEM simulations of bubbling fluidized bed 18 and riser, 21,34 and the filtered drag model has been validated for coarse CFD-DEM simulation of CFB risers. 35 Because the effects of collision parameters and the corrections to drag models have been investigated in the past, this research focuses on the uncertainties caused by statistic weight.…”

Section: Introductionmentioning

confidence: 99%

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Method to estimate uncertainty associated with parcel size in coarse discrete particle simulation

Benyahia

2018

AIChE Journal

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Coarse grained particle methods significantly reduce the computation cost of large‐scale fluidized bed simulation by lumping many real particles into a computation parcel. This research provides a method to estimate the errors associated with parcel size in large‐scale fluidized bed simulations. This uncertainty is first quantified in small scale domains by comparing results of discrete particle method with that employing coarse parcels of different sizes. Then, this uncertainty is correlated with parcel size and simulation domains consisting of a simple homogeneous cooling system and more complex bubbling and circulating fluidized beds. These correlations allow us to accurately estimate the uncertainty in large‐scale fluidized beds based solely on data obtained in smaller systems. The ability to estimate model‐related uncertainty in larger systems makes this method relevant for industrial applications. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2340–2350, 2018

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Method to estimate uncertainty associated with parcel size in coarse discrete particle simulation

Benyahia

2018

AIChE Journal

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“…Lu and colleagues [168] employed simulations of a bubbling fluidised bed to show that when parcels account for several real particles, the mesh size is significantly coarse and some sub-grid features of the motion of the solids might be not reproduced, unless structure dependent sub-grid drag models are used, such as the energy-minimisation multi-scale (EMMS)/bubbling drag model [169]: these are different from (semi-)empirical drag models and Direct Numerical Simulations-based correlations, which are homogeneous, because they take into account the effect of heterogeneous structure, such as bubbles and clusters.…”

Section: Definition Of Particlesmentioning

confidence: 99%

Modelling of Spouted and Spout-Fluid Beds: Key for Their Successful Scale Up

et al. 2017

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The development of robust mathematical models could provide the necessary tools for a more rapid, efficient, and reliable spouted bed technology development. Computer simulations can be very useful to aid this design and scale-up process: firstly, they can contribute to obtain a fundamental insight into their complex dynamic behavior by understanding the elementary physical principles such as drag, friction, dissipation etc.; secondly, the simulations can be used as a design tool where the ultimate goal is to have a numerical model with predictive capabilities for gas-particle flows at engineering scale. Clearly, one single simulation method will not be able to achieve this goal, but a hierarchy of methods modelling phenomena on different length and time scales can achieve this. The most fruitful approach will be when they are simultaneously followed, so that they can mutually benefit from each other. In this sense, this paper presents a review of the current state of the art of modelling on spouted and spout-fluid beds through an analysis of recent literature following a multiscale approach (molecular and particle, lab, plant and industrial scale). The main features of the different scales together with their current limits are discussed and specific topics are highlighted as paths that still need to be explored. In summary, the paper aims to define the theoretical setline and the basis of improvement that would lead to a robust multiscale model with solid links between micro and macroscopic phenomena. If done with the correct balance between accuracy and computational costs it will gear SB towards their reliable and successful implementation.

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An efficient and reliable predictive method for fluidized bed simulation

Benyahia

2017

AIChE Journal

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In past decades, the continuum approach was the only practical technique to simulate large-scale fluidized bed reactors because discrete approaches suffer from the cost of tracking huge numbers of particles and their collisions. This study significantly improved the computation speed of discrete particle methods in two steps: First, the time-driven hardsphere (TDHS) algorithm with a larger time-step is proposed allowing a speedup of 20-60 times; second, the number of tracked particles is reduced by adopting the coarse-graining technique gaining an additional 2-3 orders of magnitude speedup of the simulations. A new velocity correction term was introduced and validated in TDHS to solve the overpacking issue in dense granular flow. The TDHS was then coupled with the coarse-graining technique to simulate a pilot-scale riser. The simulation results compared well with experiment data and proved that this new approach can be used for efficient and reliable simulations of large-scale fluidized bed systems. V C 2017 American Institute of Chemical Engineers AIChE J, 63: 5320-5334, 2017 Figure 20. Computation speed (CPU time to simulate 10 thousand steps) using MFIX-DEM program with increased number of particles.Figure 21. Computation speed of case W125 and W1000.

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Influence of grid resolution, parcel size and drag models on bubbling fluidized bed simulation

Cited by 76 publications

References 42 publications

Method to estimate uncertainty associated with parcel size in coarse discrete particle simulation

Method to estimate uncertainty associated with parcel size in coarse discrete particle simulation

Modelling of Spouted and Spout-Fluid Beds: Key for Their Successful Scale Up

An efficient and reliable predictive method for fluidized bed simulation

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