Development of a novel two-phase pressure drop multiplier correlation of screw tube for fusion reactor safety

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Abstract 

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 over-predict the experimental values. The correlation proposed by Ramesh showed the lowest average error rate (31.94%); however, the results were under-predicted for high-heat-flux conditions. To obtain more accurate results, the authors developed a Python code using an artificial intelligence regression method to proceed with the optimization process based on the experimental values. Using this code, the authors 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.

Instructions: 

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 over-predict the experimental values. The correlation proposed by Ramesh showed the lowest average error rate (31.94%); however, the results were under-predicted for high-heat-flux conditions. To obtain more accurate results, the authors developed a Python code using an artificial intelligence regression method to proceed with the optimization process based on the experimental values. Using this code, the authors 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.

Funding Agency: 
Korea Research Foundation’s Education Human Resource Development Program
Grant Number: 
NRF-2018H1A2A1062726