In the context of nuclear reactors, understanding two-phase flow and heat and mass transfer phenomena is pivotal for ensuring operational safety, efficiency, and innovative reactor design. These phenomena, significant during regular functioning and crucial in transient scenarios such as loss-of-coolant accidents (LOCAs), present a daunting challenge due to their inherent complexity involving phase interfaces, turbulence, and multiscale interactions. Traditional simulation methods, such as empirical correlations and conventional experimental techniques, often struggle to capture complex behaviours under extreme conditions, which are prevalent in modern reactors. Recently, advancements in computational power and pioneering experimental diagnostics, such as high-speed imaging and neutron radiography, have been making strides in addressing these complexities, highlighting the need for a comprehensive synthesis of these innovations. Additionally, the recent advancements in machine learning and artificial intelligence have opened the way towards a new approach for multi-phase modelling for nuclear reactors.

This Research Topic explores advanced two-phase flow modeling and experimental techniques for nuclear reactors. The scope encompasses critical limitations in two-phase flow simulations and experiments, including enhancing the accuracy of multiscale and multiphysics models, bridging the gaps between simulation methodologies and experimental validations, and tailoring methodologies for novel nuclear technologies. The Topic also covers recent advances, such as machine learning-enhanced simulations, artificial intelligence, transient measurement sensor arrays, and fluid dynamics validation via benchmark experiments.

Research themes within the scope of this collection include, but are not limited to:
• Multiscale two-phase simulation methodologies (e.g., DNS/LES coupling, phase-field models)
• Experimental diagnostics for extreme conditions (e.g., high-pressure boiling, transient two-phase flows)
• Validation and benchmarking of numerical tools through high-quality experimental data
• Data assimilation techniques for refined simulations
• Machine learning and artificial intelligence approaches for multi-phase flows
• Applications to advanced nuclear reactor designs (e.g., small modular reactors, Gen-IV reactors).

We particularly welcome interdisciplinary works that combine nuclear engineering, fluid dynamics, and computational and/or experimental science to drive progress in the field.

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Advanced Two-Phase Flow Modeling and Experimental Techniques for Nuclear Reactors