Evaluations of boiling heat flux partitioning and bubble parameter models using a 0-D framework

Reliable prediction of boiling heat transfer remains a major challenge in thermal hydraulics due to the complex mechanisms governing phase change at heated surfaces. In this work, we develop a zero-dimensional (0-D), point-averaged framework to systematically evaluate and compare the predictive performance of multiple heat flux partitioning (HFP) formulations and bubble dynamics sub-models. 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 characterising bubble dynamics parameters such as bubble departure diameter, frequency, and nucleation site density. The framework, implemented in MATLAB as XBOIL, enables rapid and exhaustive testing of thousands of HFP and bubble parameter sub-model combinations. Model predictions are compared against an extensive experimental database covering both pool and flow boiling under a wide range of pressures, mass fluxes, and subcoolings. The approach isolates the role and interactions of individual sub-models, allowing both case-specific and global assessments of accuracy and generalisability. Results reveal that while some sub-models consistently contribute to improved predictive accuracy, no single configuration performs optimally across all conditions, highlighting compensatory effects among various closures involved in modelling boiling heat transfer. Complementary sensitivity analyses are conducted to examine the effects of flow conditions, the sensitivity of modelling parameters such as contact angle, and the influence of different sub-model selections on heat flux predictions.