A new Eulerian-Eulerian-Lagrangian solver in OpenFOAM and its application in a three-phase bubble column

Zhang, Huan; Yin, Zhifu; Chi, Wanqing; Zhang, Wenming

doi:10.1016/j.powtec.2023.118661

Cited by 3 publications

(2 citation statements)

References 65 publications

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“…In contrast, E-L models individually track the discrete particles of the disperse phase (solid and gas) within the reactor; it can work for laminar flow with low superficial gas velocity. For turbulent flow with high superficial gas velocity bubble size varies rapidly, which leads to high computational costs . However, for turbulent flow, the E-E model is the primary choice of researchers. , Effective CFD modeling requires choosing suitable interfacial forces, such as drag, lift, and virtual mass.…”

Section: Introductionmentioning

confidence: 99%

“…For turbulent flow with high superficial gas velocity bubble size varies rapidly, which leads to high computational costs . However, for turbulent flow, the E-E model is the primary choice of researchers. , Effective CFD modeling requires choosing suitable interfacial forces, such as drag, lift, and virtual mass. Drag force has a notable effect on interactions involving three phases.…”

Section: Introductionmentioning

confidence: 99%

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Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Dhakane,

Yadav

2024

Ind. Eng. Chem. Res.

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Bubble column reactors (BCRs) find extensive application in the chemical industry for facilitating three-phase reactions where most often solid-phase catalysts are employed alongside gaseous and liquid reactants. In these reactors, accurately identifying the prevailing hydrodynamic behavior is crucial for both design optimization and successful scale-up efforts. This research investigates the flow characteristics of solid−liquid−gas mixtures in cylindrical bubble columns through a comprehensive approach combining computational fluid dynamics (CFD) simulations and a CFD-based machine learning (ML) model. The three-phase Eulerian−Eulerian CFD simulations explored the effects of 5% and 20% solid loading with varying superficial gas velocities (0.1 and 0.2 m/s) on solid distribution. The obtained results align qualitatively with the experimental findings from the literature. A CFD−deep neural network (DNN) model is presented in a parallel study of a three-phase bubble column situation. By using a large data set obtained from CFD simulations, the deep neural network model is trained to accurately predict fluid dynamics closely resembling the CFD simulations. The ML architecture incorporates input data obtained from CFD results, thereby allowing the deep neural network to acquire knowledge and identify patterns in different fluid flow characteristics, such as velocity, pressure, and holdup. The tuning of hyperparameters is crucial for successful ML predictions (with R 2 values 0.97 to 0.99) in the current CFD-DNN model. Additionally, a graphical user interface (GUI) on the basis of CFD-DNN has been developed for easy model handling. The CFD-DNN model showcases its capacity to forecast hydrodynamic parameters that were not used in the training process, hence diminishing computing expenses and simulation duration in comparison with conventional CFD techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Dhakane,

Yadav

2024

Ind. Eng. Chem. Res.

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Numerical simulations of particle concentration and size effects in a slurry bubble column with a CFD‐PBM coupled model

Shen,

Zhang,

Jia

et al. 2024

AIChE Journal

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The combined effects of particle concentration (0–20 vol%) and particle size on the hydrodynamics of a slurry bubble column were studied by computational fluid dynamics coupled with population balance model (CFD‐PBM) in a wide range of superficial gas velocity (0.02–0.20 m/s). This CFD‐PBM coupled model included multiple effects of particles, namely increased slurry viscosity, reduced drag force, promoted bubble coalescence, and attenuated liquid turbulence, among which turbulence attenuation was crucial. To quantify liquid turbulence attenuation, a mechanistic model was established by equating the energy dissipated by particle motion to the attenuated liquid turbulence energy calculated from turbulent energy spectrum. The turbulent energy dissipation rate was reduced by over 60% at a solid concentration of 20%. Using this model, the effects of particle concentration and particle size on overall gas holdup were well predicted. This model advanced our understanding of how particles affect the gas holdup in a slurry bubble column.

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CFD-PBM simulations the effect of aeration rates on hydrodynamics characteristics in a gas-liquid-solid aerobic fluidized bed biofilm reactor

Ren,

Pei,

Zhou

et al. 2024

Powder Technology

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A new Eulerian-Eulerian-Lagrangian solver in OpenFOAM and its application in a three-phase bubble column

Cited by 3 publications

References 65 publications

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Numerical simulations of particle concentration and size effects in a slurry bubble column with a CFD‐PBM coupled model

CFD-PBM simulations the effect of aeration rates on hydrodynamics characteristics in a gas-liquid-solid aerobic fluidized bed biofilm reactor

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