The core of the very high temperature reactor (VHTR) PMR200 (a prismatic modular reactor rated at 200MW of thermal power) consists of hexagonal prismatic fuel blocks and reflector blocks made of graphite. If the core bypass flow ratio increases, the coolant channel flow is decreased and can then lower the heat removal efficiency, resulting in a locally increased fuel block temperature. The coolant channels in the fuel blocks are connected to bypass gaps by the cross gap, complicating flow distribution in the VHTR core. Therefore, reliable estimation of the bypass flow is highly important for the design and safety analysis of the VHTR core. Because of the complexity of the core geometry and gap configuration, it is challenging to predict the flow distribution in the VHTR core. To analyze this flow distribution accurately, it is necessary to determine the cross flow phenomena, and the loss coefficient across the cross gap has to be evaluated to determine the flow distribution in the VHTR core when a lumped parameter code or a flow network analysis code that uses the correlation of the loss coefficient is employed. The purpose of this paper is to develop a loss coefficient correlation applicable to the cross gap in the PMR200 core. The cross flow was evaluated experimentally using the difference between the measured inlet and outlet mass flow rates. Next, the applicability of a commercial computational fluid dynamics (CFD) code, CFX 15, was confirmed by comparing the experimental data and CFD analysis results. To understand the cross flow phenomena, the loss coefficient was evaluated; in the high Reynolds number region, the cross flow loss coefficient on Reynolds number is nearly constant regardless of the Reynolds number, whereas it varies with the gap size in the low Reynolds number region. Finally, the loss coefficient correlation was proposed on the basis of experiments and the CFD analysis. The developed correlation was compared with existing correlations, and the developed one shows better agreement with the experimental results than the existing ones. Hence, the developed correlation will be applied to the flow network code to analyze flow distribution in the PMR200 core.
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