Computational and Experimental Advances in Multiphase Flow

Multiphase flow is one of the most important area of fluid mechanics, concerning the simultaneous flow of two or more phases, such as solid–liquid, gas–solid, and gas–liquid systems. Such flows are widely come across in various engineering and industrial applications, including slurry transportation, hydraulic systems, thermal power plants, chemical reactors, and oil and gas pipelines etc. The interaction between different phases significantly affects flow behaviour, heat transfer, erosion, pressure drop, and turbulence characteristics etc. Over the past few years, several experimental techniques, theoretical approaches, and significant advancements in Computational Fluid Dynamics (CFD) have been developed to better understand the complexity of multiphase flow phenomena. New advancements in computational tools, modern experimental visualization techniques, Artificial Intelligence (AI), and Machine Learning (ML) are further creating new thrust area’s and opportunities to improve the prediction, analysis, and understanding of complex multiphase flow systems.The goal of this Research Topic is to explore the existing challenges related to the accurate prediction and analysis of multiphase flow problems associated with complex engineering systems. Despite rapid advancements in experimental techniques and Computational Fluid Dynamics (CFD), accurate prediction of flow behaviour still remains a major challenge due to the transient and nonlinear nature of interactions between different phases. The objective of this Research Topic is to bring together the latest research in the field of multiphase flow involving Computational Fluid Dynamics (CFD), experimental methods, data-driven techniques, Artificial Intelligence (AI), and Machine Learning (ML) to achieve a better understanding of the complex interactions between phases. Special attention will be given to experimentally and computationally validated models that have the potential for large-scale industrial applications.The scope of this Research Topic welcomes original research articles, case studies, and review papers focusing on recent advancements in the field of multiphase flow using modern experimental and computational techniques. The scope of the topic includes, but is not limited to, phase modelling, erosive wear prediction, particle mixing, particle transport, slurry flow, turbulence modelling, visualization techniques, and fluid–particle interaction. Articles involving innovative techniques, advanced methodologies, real-time industrial applications, validation studies, data-driven approaches, Artificial Intelligence (AI), and Machine Learning (ML) techniques are highly encouraged. Authors' contributions involving both computational and experimental approaches for better understanding and prediction of complex multiphase flow phenomena are particularly welcome. Topic editors: Om Parkash, Gopal Krishan Source: https://www.frontiersin.org/research-topics/82123/computational-and-experimental-advances-in-multiphase-flow