subcooled flow
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2022 ◽  
Vol 172 ◽  
pp. 107347
Haidong Liu ◽  
Erlei Zhao ◽  
Deqi Chen ◽  
Jiang Qin ◽  
Peigang Yan ◽  

Wei-Ting Hsu ◽  
Namkyu Lee ◽  
Donghwi Lee ◽  
JeongJu Kim ◽  
Maroosol Yun ◽  

2021 ◽  
Ji Hwan Lim ◽  
Minkyu Park

Abstract For the cooling system of the future, nuclear fusion tokamak, to operate stably and continuously, it is important to identify potential hazards that may occur in the system in advance. Among the various potential hazards associated with the nuclear fusion tokamak, the onset of nucleate boiling (ONB) is a point at which the heat-transfer mechanism changes dramatically and is a crucial factor that must be addressed. In particular, the equipment inside the tokamak is loaded with a heat flux of several MW/m2 under single-side heating conditions, and it is important to predict the ONB under these special heating conditions. Therefore, in this study, the ONB of a flat heat sink was experimentally investigated under highly subcooled flow conditions. Based on the physical understanding of the thermo-hydraulic aspect of the ONB, the wall temperature gradient change point, which is mainly used in the subcooled flow condition, was selected as the ONB detection criterion. Trends in the ONB heat flux change were analyzed as representative system parameters that can be tuned in the cooling system, such as subcooling, mass flow rate, and pressure. In addition, the ONB correlations developed in the previous studies were evaluated for predicting the performance under one-side high heat load conditions. However, the large difference in the experimental conditions (range of system parameters and heating conditions) and the fact that the influence of system parameters was not reflected in the correlation resulted in high error rates. Therefore, the ONB correlation that can be used in the tokamak heat flux condition was newly developed through a dimensional analysis that can effectively reflect the influences in the correlation through dimensionless numbers. The developed correlation can be of great help in designing a diverter or blanket cooling system and establishing an operational strategy.

2021 ◽  
Vol 2119 (1) ◽  
pp. 012053
A. S. Shamirzaev

Abstract An experimental study of the pressure drop under subcooled flow boiling of the refrigerant R141b in a system with two slotted microchannels was carried out. A copper block with two microchannels 2 mm wide, 0.4 mm deep, and 16 mm long was used as an experimental section for testing. The mass flow rate varied in the range from 1 to 4 g/s, the initial subcooling from 20°C to 50°C. Experimental data show a significant decrease in the pressure drop when the critical heat flux is reached. The experimental data are compared with the model known from the literature. Experimental data show that the occurrence of nucleate boiling incipience at subcooled boiling corresponds to a larger heat flux than that given by the recommended correlation.

2021 ◽  
Ji Hwan Lim ◽  
Su Won Lee ◽  
Hoongyo Oh ◽  
Minkyu Park ◽  
Donkoan Hwang ◽  

Abstract In this study, the onset of flow instability (OFI) heat flux of a one-side heated swirl tube is experimentally investigated. The OFI heat flux means the minimum heat flux that can cause flow instability by the vapor generated in the flow path. An analysis of the effect of system parameters on the OFI heat flux indicates that as the pressure increases, the bubble size decreases. Therefore, the void fraction decreases and, consequently, the OFI heat flux tends to increase. Similarly, the higher the flow rate and degree of subcooling, the faster the vapor can be removed; thus, the OFI heat flux increases. In addition, the prediction performances of the existing OFI correlations developed under the subcooled flow-boiling condition are evaluated. Therefore, although the Wang correlation indicates the lowest error rate, it yields a high mean absolute error rate of 87.75%. Thus, it is difficult to predict the OFI heat flux of a one-side heated swirl tube using the existing OFI correlations. Therefore, in this study, a new correlation is developed using a Python code created by employing an artificial intelligence regression method. The developed correlation incorporates the impact of one-side heating, swirl tape, mass flow rate, subcooling, and pressure (mean absolute error = 12.17%, root mean square error = 14.99%).

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