Multiphase Flow Simulation in Energy Systems: A Review of Computational Fluid Dynamics (CFD) Techniques and Challenges

Onyiriuka frank¹, Oseni Temitope Damilola², Ochuele Dominic Agida³, Moses Adondua Abah³ and Micheal Oladosu Abimbola³

¹Department of Mechanical Engineering, Southern Delta University, Ozoro, Delta State, Nigeria

²Department of Mechanical Engineering, Kwara State University, Kwara State, Nigeria.

³ResearchHub Nexus Institute, Nigeria

(✉) Corresponding Author: Moses Adondua Abah; m.abah@fuwukari.edu.ng

Received: Aug 12, 2026/ Revised: Sept 13 2026/Accepted: Sept 16, 2026

Highlights

CFD enables detailed simulation of multiphase flows in diverse energy systems.
Eulerian–Eulerian and Eulerian–Lagrangian frameworks support complex multiphase flow modeling.
VOF, Level Set, and Phase Field methods improve prediction of phase interfaces.
Turbulence modeling, mesh quality, discretization, and validation strongly affect CFD reliability.
CFD supports energy-system design, performance optimization, and operational safety.
Major challenges include multiscale coupling, computational cost, and predictive uncertainty.
AI-assisted CFD, high-performance computing, reduced-order models, and digital twins are emerging tools for advanced simulation.

Abstract

Multiphase flow phenomena underpin the performance of numerous energy technologies, including oil and gas production, nuclear reactors, thermal power systems, carbon capture processes, and emerging hydrogen energy applications. Computational fluid dynamics (CFD) has become an indispensable tool for investigating these complex flows by enabling detailed prediction of interfacial dynamics, phase interactions, heat and mass transfer, and transport behavior under diverse operating conditions. This review critically examines contemporary CFD techniques employed for multiphase flow simulation in energy systems, with emphasis on the strengths, limitations, and application domains of major modeling approaches. Eulerian–Eulerian and Eulerian–Lagrangian frameworks, interface-capturing methods such as the Volume of Fluid, Level Set, and Phase Field techniques, and emerging particle-based and Lattice Boltzmann methods are comparatively assessed. The review further discusses essential numerical modeling strategies, including turbulence modeling, interfacial force representation, mesh generation, discretization schemes, solver selection, convergence assessment, and model verification and validation. Recent applications across conventional and low-carbon energy systems are synthesized to illustrate the growing role of CFD in system design, performance optimization, and operational safety. Key challenges remain in accurately resolving complex interfaces, coupling multiscale and multiphysics phenomena, reducing computational cost, and improving predictive reliability through robust experimental validation and uncertainty quantification. Finally, emerging advances in artificial intelligence-assisted CFD, high-performance computing, physics-informed reduced-order models, and digital twin technologies are highlighted as promising directions for next-generation multiphase flow simulation. By integrating recent methodological developments with practical energy applications, this review provides a concise and comprehensive reference for researchers and engineers seeking to advance CFD-driven analysis and optimization of multiphase energy systems.

Keywords: Computational fluid dynamics (CFD), Multiphase flow simulation, Energy systems, Interface-capturing methods, Numerical modeling, and Digital twins

References

Aihara, S., Takada, N. and Takaki, T. (2023). Highly conservative Allen–Cahn-type multi-phase-field model and evaluation of its accuracy. Theoretical and Computational Fluid Dynamics. 37: 639–659. https://doi.org/10.1007/s00162-023-00655-0

Arnold, T. F. and Höhne, T. (2026). Validation of the AIAD model in ANSYS Fluent for horizontal two-phase flows. Experimental and Computational Multiphase Flow. https://doi.org/10.1007/s42757-025-0263-0

Chen, J. T., Xiao, W., Zhao, W., Zhang, P. H., Yang, F. J., Jin, T. et al. (2023). Advances in verification and validation in computational fluid dynamics. Advances in Mechanics. 53(3): 626-660 doi: 10.6052/1000-0992-23-012

Cheng, L., Guo, Z. and Xia, G. (2024). A Review on Research and Technology Development of Green Hydrogen Energy Systems with Thermal Management and Heat Recovery. Heat Transfer Engineering. 45(4-5): 300-322. https://doi.org/10.1080/01457632.2023.2191435

Chew, J. W., Syamlal, M., Andersson, R. and Cocco, R. (2026). Modeling of industrial multiphase reactors. Current Opinion in Chemical Engineering. 51: 101223. https://doi.org/10.1016/j.coche.2025.101223

De la Cruz-Ávila, M. and Bonasia, R. (2025). Enhanced 3D Turbulence Models Sensitivity Assessment Under Real Extreme Conditions: Case Study, Santa Catarina River, Mexico. Hydrology. 12(10):260. https://doi.org/10.3390/hydrology12100260

Efa, D. A. and Ifa, D. A. (2025). Systematic review of multiscale thermal prediction models for FDM/FFF additive manufacturing: Toward digital twin integration with physics-informed machine learning. Results in Engineering. 28: 108087. https://doi.org/10.1016/j.rineng.2025.108087

El-Emam, R. S., Constantin, A., Bhattacharyya, R., Ishaq, H. and Ricotti, M. E. (2024). Nuclear and renewables in multipurpose integrated energy systems: A critical review. Renewable and Sustainable Energy Reviews. 192: 114157. https://doi.org/10.1016/j.rser.2023.114157

Frederix, E. M. A. (2026). Progress and challenges in multiphase CFD method development for nuclear thermal hydraulics. Nuclear Technology. https://doi.org/10.1080/00295450.2025.2572006

Ghannam, A., Chehade, A., Generous, M. M., Alazzam, A., Kleinstreuer, C., Ahmadi, G. and Abu-Nada, E. (2025). A comprehensive review of particle-laden flows modeling: Single/multiphase modeling approaches, benchmarks, heat transfer, intermolecular interactions, recent advances and future directions. Physics Reports. 1118–1120: 1–96. https://doi.org/10.1016/j.physrep.2025.03.001

He, Q., Cheng, Y., Hu, F., Huang, W. and Li, D. (2024). A Comparative Study of Hydrodynamic Lattice Boltzmann Equation in Phase-Field-Based Multiphase Flow Models. Communications in Computational Physics. 35(4): 859-904. https://doi.org/10.4208/cicp.RE-2023-0066

Kind, J., Sielaff, A. and Stephan, P. (2025). Physical modeling of conjugate heat transfer for multiregion and multiphase systems with the Volume-of-Fluid method. Engineering with Computers. 41: 761–783. https://doi.org/10.1007/s00366-024-02051-6

Kjeldsberg, H. A., Sundnes, J. and Valen-Sendstad, K. (2023). A verified and validated moving domain CFD solver with applications to cardiovascular flows. arXiv. https://arxiv.org/abs/2305.15539

Li, J., Zheng, D. and Zhang, W. (2023). Advances of Phase-Field Model in the Numerical Simulation of Multiphase Flows: A Review. Atmosphere. 14(8):1311. https://doi.org/10.3390/atmos14081311

Liu, C. H., Massarweh, O. and Abushaikha, A. S. (2025). Numerical simulation on geosystem Thermal-Hydro-Mechanical-Chemical coupling: A review. Geoenergy Science and Engineering. 254: 214059. https://doi.org/10.1016/j.geoen.2025.214059

Lu, P., Zhao, L., Zheng, N., Liu, S., Li, X., Zhou, X. and Yan, J. (2022). Progress and prospect of flow phenomena and simulation on two-phase separation in branching T-junctions: A review. Renewable and Sustainable Energy Reviews. 167: 112742. https://doi.org/10.1016/j.rser.2022.112742

Luo, K., Zhao, J., Wang, Y., Li, J., Wen, J., Liang, J. et al. (2025). Physics-informed neural networks for PDE problems: A comprehensive review. Artificial Intelligence Review. 58: 323. https://doi.org/10.1007/s10462-025-11322-7

Mahmoud, A., Al-Dogail, A. and Gajbhiye, R. (2026). A Comprehensive Review of Flow Assurance in the Energy Transition: Flow Loop Platforms and AI-Driven Solutions for Hydrogen, CO2, and Ammonia Transport. Arabian Journal for Science and Engineering. 51(12):14587-14612. https://doi.org/10.1007/s13369-025-10758-x

Malikov, Z. M., Madaliev, M. E., Chernyshev, S. L., et al. (2024). Validation of a two-fluid turbulence model in COMSOL Multiphysics for the problem of flow around aerodynamic profiles. Scientific Reports. 14: 2306. https://doi.org/10.1038/s41598-024-52673-5

Muralha, J., Eça, L. and Klaij, C. (2023). Verification of a Pressure-Based Compressible Flow Solver. International Journal of Computational Fluid Dynamics. 37(1):1–27. https://doi.org/10.1080/10618562.2023.2242271

Ni, R. (2024). Deformation and breakup of bubbles and drops in turbulence. Annual Review of Fluid Mechanics. 56: 319–347. https://doi.org/10.1146/annurev-fluid-121021-034541

Oliveira, J. P. S., Medronho, R. A., Santos, F. P. and Klein, T. S. (2025). CFD multiphase modeling of liquid–liquid hydrocyclones: A review. Journal of Industrial and Engineering Chemistry. 145:33–62. https://doi.org/10.1016/j.jiec.2024.10.044

Padula, G., Girfoglio, M. and Rozza, G. (2024). A brief review of reduced order models using intrusive and non-intrusive techniques. Proceedings in Applied Mathematics and Mechanics. 24: e202400210. https://doi.org/10.1002/pamm.202400210

Piredda, M. M. and Asinari, P. (2025). Lattice Boltzmann Framework for Multiphase Flows by Eulerian–Eulerian Navier–Stokes Equations. Computation. 13(7): 164. https://doi.org/10.3390/computation13070164

Porombka, P. and Schlottke, J. (2025). Euler–Euler simulation of multi-regime two-phase flows with thin liquid films. Experimental and Computational Multiphase Flow. 7:197–210. https://doi.org/10.1007/s42757-024-0227-9

Pozorski, J. and Olejnik, M. (2024). Smoothed particle hydrodynamics modelling of multiphase flows: An overview. Acta Mechanica. 235(5):1685–1714. https://doi.org/10.1007/s00707-023-03763-4

Qian, Z., Yang, T. and Liu, M. (2024). An Overview of Coupled Lagrangian–Eulerian Methods for Ocean Engineering. J. Marine. Sci. Appl. 23: 366–397. https://doi.org/10.1007/s11804-024-00404-7

Raje, P., Parish, E., Hickey, J.-P., Cinnella, P. and Duraisamy, K. (2024). Recent developments and research needs in turbulence modeling of hypersonic flows. arXiv. https://arxiv.org/abs/2412.13985

Sadeghiseraji, J., Garcia-Vilchez, M., Castilla, R. and Raush, G. (2024). Recent Advances in Numerical Simulation of Ejector Pumps for Vacuum Generation—A Review. Energies. 17(17):4479. https://doi.org/10.3390/en17174479

Sharma, P., Chung, W. T., Akoush, B. and Ihme, M. (2023). A Review of Physics-Informed Machine Learning in Fluid Mechanics. Energies. 16(5):2343. https://doi.org/10.3390/en16052343

Shi, Y., Wei, P., Feng, K., Beer, M., et al. (2025). A survey on machine learning approaches for uncertainty quantification of engineering systems. Machine Learning for Computational Science and Engineering. 1: 11. https://doi.org/10.1007/s44379-024-00011-x

Wang, H., Cao, Y., Huang, Z., Liu, Y., Hu, P., Luo, X. et al. (2024). Recent advances on machine learning for computational fluid dynamics: A survey. arXiv. https://arxiv.org/abs/2408.12171

Wang, Z., Liu, Y., Ji, Y., Wang, T. and Zhao, C. (2023). Recent advances in CFD simulations of multiphase flow processes with phase change. Industrial & Engineering Chemistry Research. 62:12355–12386. https://doi.org/10.1021/acs.iecr.3c00706

Wu, H., Wang, X., Su, W., Yang, S. and Li, Z. (2026). Machine Learning-based Reduced-Order Model and multi-fidelity approaches for field reconstruction in CFD: A review. Chemical Engineering Science. 322:123156. https://doi.org/10.1016/j.ces.2025.123156.

Wu, M., Zhang, J., Gui, N., Zou, Q., Yang, X., Tu, J. et al. (2024). Advances in the modeling of multiphase flows and their application in nuclear engineering—A review. Experimental and Computational Multiphase Flow, 6(4), 287–352. https://doi.org/10.1007/s42757-024-0202-5

Yang, X., Xi, T., Qin, Y., Zhang, H. and Wang, Y. (2024). Computational Fluid Dynamics–Discrete Phase Method Simulations in Process Engineering: A Review of Recent Progress. Applied Sciences. 14(9): 3856. https://doi.org/10.3390/app14093856

Yarahmadi, S., Babazadeh Asbagh, G. and Jafari, M. (2025). Computational fluid dynamics (CFD) in multiphase heat transfer: A critical systematic review of methods and applications. International Communications in Heat and Mass Transfer. Advance online publication.

Yarahmadi, S., Babazadeh Asbagh, G. and Jafari, M. (2026). Computational fluid dynamics (CFD) in multiphase heat transfer: A critical systematic review of methods and applications. International Communications in Heat and Mass Transfer. 110833. https://doi.org/10.1016/j.icheatmasstransfer.2026.110833

Zahid, F. and Cunningham, J. A. (2025). Review of the color gradient lattice Boltzmann method for simulating multiphase flow in porous media: Viscosity, gradient calculation, and fluid acceleration. Fluids. 10(5): 128. https://doi.org/10.3390/fluids10050128

Zhang, D., Anjum, T., Chu, Z., Cross, J. S. and Ji, G. (2025). Simulation of multiphase flow with thermochemical reactions: A review of computational fluid dynamics (CFD) theory to AI integration. Renewable and Sustainable Energy Reviews. 221: 115895. https://doi.org/10.1016/j.rser.2025.115895

How to cite this article

Onyiriuka, F., Oseni, T. D., Agida, O. D., Abah, M. A., & Abimbola, M. O. (2026). Multiphase flow simulation in energy systems: A review of computational fluid dynamics (CFD) techniques and challenges. Science Archives, 7(3), 325–336. https://doi.org/10.47587/SA.2026.7304

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