Abstract

When the critical heat flux (CHF) is reached, the heat transfer efficiency rapidly decreases. Therefore, CHF must be considered as a potential hazard for a stable cooling system operation. In particular, securing the thermal margin of plasma-facing-components inside the tokamak loaded with ultra-high heat flux is directly related to system safety. According to this necessity, in this study, CHF of flat rectagular channel under one-side high-heat-load condition was experimentally explored. As a result of analyzing the effect of system parameters on CHF, it was observed that mass flow rate and sub-cooling had a proportional relationship with CHF, whereas pressure had an inverse relationship with CHF. This is because a high mass flow rate and high sub-cooling promote forced convection heat transfer and quickly remove vapors from the flow path, but an increase in pressure reduces liquid surface tension and latent heat, causing early boiling. In addition, the predicton performance of the existing sub-cooled flow boiling CHF correlations was evaluated under the one-side high heat load condition. However, the large difference in experimental conditions and the structural difference between circular tube and rectangular channel made them under-predict the experimental values. Accordingly, we developed new optimized CHF correlations using a Python code grafted with machine learning technology. The developed correatlion can be utilized when evaluating the thermal safety margin of the future tokamak, and can be a great help when establishing a cooling system operation strategy.

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