Assessment of Heat Flux Partitioning Approaches for the Prediction of Subcooled Flow Boiling

This paper presents a point-averaged, zero-dimensional framework for nucleate boiling heat transfer, enabling systematic assessment of multiple heat flux partitioning (HFP) models in tandem with diverse bubble dynamics sub-models, often used in multiphase CFD to predict subcooled flow boiling. HFP models decompose the total heat transfer into individual components characterising various heat transfer mechanisms, such as evaporation, quenching and single-phase convection. This approach relies on sub-models for parameters like bubble departure diameter and frequency to capture these mechanisms accurately. We propose a computationally efficient way to test a large number of sub-models and explore thousands of sub-model combinations using the zero-dimensional framework. The framework also avoids having to account for the coupling with interface transfer closures or population balance models, that makes assessing boiling models inside multiphase CFD challenging. Comparisons with experimental data expose the strengths and limitations of the different model formulations, highlighting the critical impact of sub-model choice on the predictive accuracy. Through these analyses, the point-averaged approach provides valuable guidance for refining model closures and targeting further validation.

Proceedings of 21st International Topical Meeting on Nuclear Reactor Thermal Hydraulics