A study of nucleate boiling phenomena on nano/microstructures is a very basic and useful study with a view to the potential application of modified surfaces as heating surfaces in a number of fields. We present a detailed study of boiling experiments on fabricated nano/microstructured surfaces used as heating surfaces under atmospheric conditions, employing identical nanostructures with two different wettabilities (silicon-oxidized and Teflon-coated). Consequently, enhancements of both boiling heat transfer (BHT) and critical heat flux (CHF) are demonstrated in the nano/microstructures, independent of their wettability. However, the increment of BHT and CHF on each of the different wetting surfaces depended on the wetting characteristics of heating surfaces. The effect of water penetration in the surface structures by capillary phenomena is suggested as a plausible mechanism for the enhanced CHF on the nano/microstructures regardless of the wettability of the surfaces in atmospheric condition. This is supported by comparing bubble shapes generated in actual boiling experiments and dynamic contact angles under atmospheric conditions on Teflon-coated nano/microstructured surfaces.

In this study, the heat transfer characteristics of one-side heated screw tubes were analyzed using subcooled flow boiling experiments. The experiments were performed until the critical heat flux was reached, while the applied heat flux was gradually increased. Subsequently, the boiling curves derived from the experimental results were divided into partial nucleate boiling (PDB) and fully developed nucleate boiling (FDB) regimes. As the flow rate increases, the forced convective heat transfer is enhanced, improving the single-phase heat transfer performance. However, as the FDB region is approached, most of the heat transfer proceeds via nucleate boiling heat transfer, so the heat transfer coefficient curve converges into the same straight line. The same trend was observed when analyzing the effect of the degree of subcooling. However, as the system pressure increased, and the liquid surface tension and latent heat decreased, a faster transition was observed from the single-phase regime to the PDB regime. From evaluating the prediction performance of the existing subcooled flow boiling heat transfer correlations, among the PDB regime; Liu and Winterton correlation, a kind of Chen-type correlation, showed the highest accuracy with mean absolute error (MAE) of 13.62% and root mean square error (RMSE) of 16.13%. However, most of the FDB correlations did not accurately predict the heat transfer performance of the one-side heated screw tube under a high heat flux load of several MW/m². Therefore, a new screw tube FDB correlation was developed using Python code combined with artificial intelligence technology. The authors believe that the proposed correlation will be useful for not only predicting the heat transfer performance in the FDB region, but also establishing an operating map of the screw tube in the future.

In this study, the two-phase pressure drop in a one-sided high-heat-loaded screw tube was analyzed under subcooled flow boiling conditions. When the loaded heat flux is gradually increased under one-sided heating conditions, although the pressure drop changes slightly in the single phase, it starts to increase when loading above the onset of significant void (OSV) heat flux. The effect of system parameters on the two-phase pressure drop in relation to the change of heat transfer mechanism or fluid properties was analyzed according to each variable change. In addition, this study evaluated the prediction performance of existing two-phase pressure drop multiplier correlations developed under subcooled flow boiling conditions. Because most existing correlations are based on a smooth channel with a small diameter, they tend to overpredict the experimental values. The correlation proposed by Ramesh showed the lowest average error rate (31.94%); however, the results were underpredicted for high-heat-flux conditions. To obtain more accurate results, we developed a Python code using an artificial intelligence regression method to proceed with the optimization process based on the experimental values. Using this code, we developed a novel two-phase pressure drop multiplier correlation for a one-side-heated screw tube that can be used in fusion divertors under high-heat-load conditions.