Computational Fluid Dynamics Modeling of Bubble Column Reactors: Review

Abstract Bubble column reactors are essential equipment in the process industries, with a wide range of applications, but their hydrodynamics and design are very complex. For the past five decades, researchers and industry have relied on Computational Fluid Dynamics (CFD) to predict the local hydrodynamics of multiphase reactors, developing predictive tools capable of capturing the complex gas–liquid interactions occurring within them. Conventional design practices are primarily based on empirical and semi-empirical correlations that provide global quantities of design parameters, including gas holdup, interfacial area, mass and heat transfer coefficients, etc. These correlations depend on the operating parameters and gas–liquid systems for which they were developed, limiting their general applicability and making CFD an increasingly important tool for understanding the behavior in bubble columns. A large body of literature has been analyzed in this review to cover state-of-the-art modeling approaches. Following an introduction to the history of bubble columns, scale-up challenges, and operating flow regimes, the discussion then transitions to modeling methodologies, including the algebraic slip mixture model, the Eulerian–Lagrangian and Eulerian–Eulerian frameworks. Particular attention is given to interphase momentum transfer, including the drag force (with swarm correction), lift force, virtual mass force, turbulent dispersion force, and wall lubrication force. The significance of turbulence modeling is also considered through Reynolds-Averaged Navier–Stokes (RANS), Large Eddy Simulation (LES), Reynolds Stress Model (RSM), and formulation methodologies of bubble-induced turbulence. Population Balance Modeling (PBM) is examined as a significant tool for the prediction of bubble size distribution and integrating it with CFD through bubble breakup and coalescence kernels. Numerical factors affecting the simulation’s predictive capabilities, including dimensionality, grid resolution, discretization methodologies, and transient solution strategies, are also taken into account. Numerical investigations reported in the literature over the past 25 years have been examined, including various model configurations, their predictive capabilities, and their limitations. A clear agreement between simulated results and the operation of real bubble columns, particularly through comparisons of gas holdup, velocity profiles, bubble size distribution, mixing, heat transfer, and mass transfer, has been established. The review shows that CFD has become a powerful tool for bubble column analysis but is still limited by closure-model uncertainty, scale dependence, and the need for systematic validation under industrially relevant conditions.

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Publication Details

Journal
Industrial & Engineering Chemistry Research
Published
2026-10-16
DOI
https://doi.org/10.1021/acs.iecr.6c03325
Primary Topic
Fluid Dynamics and Mixing
Type
article
Field-Weighted Citation Impact
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article

Computational Fluid Dynamics Modeling of Bubble Column Reactors: Review

Jyeshtharaj Bhalchandra Joshi, Channamallikarjun Sidramayya Mathpati, Sarang G. Tamhankar
Industrial & Engineering Chemistry Research
Fluid Dynamics and Mixing
article

Computational Fluid Dynamics Modeling of Bubble Column Reactors: Review

Jyeshtharaj Bhalchandra Joshi, Channamallikarjun Sidramayya Mathpati, Sarang G. Tamhankar
article en

Abstract

Abstract Bubble column reactors are essential equipment in the process industries, with a wide range of applications, but their hydrodynamics and design are very complex. For the past five decades, researchers and industry have relied on Computational Fluid Dynamics (CFD) to predict the local hydrodynamics of multiphase reactors, developing predictive tools capable of capturing the complex gas–liquid interactions occurring within them. Conventional design practices are primarily based on empirical and semi-empirical correlations that provide global quantities of design parameters, including gas holdup, interfacial area, mass and heat transfer coefficients, etc. These correlations depend on the operating parameters and gas–liquid systems for which they were developed, limiting their general applicability and making CFD an increasingly important tool for understanding the behavior in bubble columns. A large body of literature has been analyzed in this review to cover state-of-the-art modeling approaches. Following an introduction to the history of bubble columns, scale-up challenges, and operating flow regimes, the discussion then transitions to modeling methodologies, including the algebraic slip mixture model, the Eulerian–Lagrangian and Eulerian–Eulerian frameworks. Particular attention is given to interphase momentum transfer, including the drag force (with swarm correction), lift force, virtual mass force, turbulent dispersion force, and wall lubrication force. The significance of turbulence modeling is also considered through Reynolds-Averaged Navier–Stokes (RANS), Large Eddy Simulation (LES), Reynolds Stress Model (RSM), and formulation methodologies of bubble-induced turbulence. Population Balance Modeling (PBM) is examined as a significant tool for the prediction of bubble size distribution and integrating it with CFD through bubble breakup and coalescence kernels. Numerical factors affecting the simulation’s predictive capabilities, including dimensionality, grid resolution, discretization methodologies, and transient solution strategies, are also taken into account. Numerical investigations reported in the literature over the past 25 years have been examined, including various model configurations, their predictive capabilities, and their limitations. A clear agreement between simulated results and the operation of real bubble columns, particularly through comparisons of gas holdup, velocity profiles, bubble size distribution, mixing, heat transfer, and mass transfer, has been established. The review shows that CFD has become a powerful tool for bubble column analysis but is still limited by closure-model uncertainty, scale dependence, and the need for systematic validation under industrially relevant conditions.

Industrial & Engineering Chemistry Research
Institute of Chemical Technology (IN)
Openalex Percentile: Top 24%
Fluid Dynamics and Mixing
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