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Xiao, Y.*; Shen, X.*; Miwa, Shuichiro*; Sun, Haomin; Hibiki, Takashi*
Konsoryu Shimpojiumu 2018 Koen Rombunshu (Internet), 2 Pages, 2018/08
In order to develop constitutive equations of two-fluid model in rod bundle flow channels, experiments of adiabatic air-water upward two-phase flow in 6
6 rod bundle flow channel were performed. Local flow parameters such as void fraction, interfacial area concentration (IAC) and so on were measured by a double-sensor optical probe. The area-averaged void fraction and IAC data were compared with the predictions from a drift-flux model and an IAC correlation.
Murata, Tetsuya*; Miwa, Shuichiro*; Sakashita, Hiroto*; Mori, Michitsugu*; Kasahara, Seiji; Yan, X.
no journal, ,
Application of a high temperature gas-cooled reactor (HTGR) for snow melting and district heating in Hokkaido was investigated. Concept design of a heat delivery system, modeling of pipes and a heat exchanger, calculation of heat supply amount from HTGR, and determination of location of the HTGRs were carried out. Sapporo and Ishikari were assumed as a heat demand district. To supply the maximum heat demand 435 MW in a year, 2 GTHTR300s, a kind of design of HTGR, were required. Though the distance from the GTHTR300 site and the heat demand district was 40 km, the temperature of the GTHTR300 waste heat was enough for the district heating. Double pipe for the heat transportation from the GTHTR300 to the district was advantageous for less heat loss and smaller excavation area. This system required 9 double pipes and more that 5000 heat exchangers.
Yamashita, Susumu; Zhou, W.*; Miwa, Shuichiro*
no journal, ,
The development of data-driven plant safety assessment methods by integrating artificial intelligence (AI) technology and thermal hydraulics has been attracting attention as a way to reduce the time cost, range of applicability to actual phenomena, and accuracy variability that are problems in developing physical models in the field of thermal hydraulics. To contribute to developing a data-driven analysis method that automatically interprets the results of CFD calculations using AI technology, training images for building an AI model were generated by analyzing water jet into a pool system using JUPITER. The analysis was conducted using the several parameters (nozzle height, jet velocity, etc). In this presentation, we will present the calculation results for building the AI model, a quantitative comparison of the penetration length with the experimental results, problems in simulating the water impingement phenomenon, and a prediction of the penetration length by machine learning.