Enhanced LES-based turbulent subcooled flow boiling prediction

High heat transfer rates in many cooling systems are often achieved through subcooled boiling flows along heated walls, where boiling ensures the system’s thermal integrity. Understanding boiling mechanisms in turbulent subcooled flows, and improving the accuracy of predictive methods is, in particular, critical for enhancing the design and increasing the safety margins of existing nuclear reactor designs, and accelerating the development and roll out of new designs. This study evaluates two boiling models coupled with large eddy simulations based on a dynamic subgrid-scale model. One boiling model utilises a mechanistic force balance approach to predict bubble dynamics, considering bubble growth and detachment influenced by micro-layer evaporation, superheated liquid heat transfer, and condensation on the bubble cap. The other model employs a reduced correlation-based method to estimate bubble departure diameter and frequency, aiming to maintain reasonable accuracy while reducing computational cost and calibration requirements. Both models are validated against experimental data for vertically upward subcooled boiling flows of water and refrigerant R12 across a wide range of operating conditions. Results show that both approaches achieve satisfactory predictive accuracy and outperform an equivalent Reynolds-averaged Navier-Stokes approach. The mechanistic model provides superior precision in capturing bubble dynamics but at higher computational expense, whereas the correlation-based model offers an efficient alternative suitable for some engineering applications. These findings advance the development of high-fidelity boiling models for nuclear thermal hydraulic simulations and provide a foundation for future research developments and industrial applications.