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• This study utilizes the increasing flow regime experimental data to extend the prediction range and improve the accuracy. • Integrated flow regime prediction models considering vertical and horizontal flow direction were developed and evaluated. • The MLP, RF, and SVM algorithms were utilized to develop the flow regime prediction model respectively. • The flow regime prediction model shows better performance than existing models in three directions respectively. The nuclear reactor core is the pivotal component in a nuclear power plant to generate and transfer heat, so accurate prediction of reactor core flow and heat transfer characteristics is a crucial problem for the reactor system design. Flow regime is closely related to the thermal–hydraulic characteristics of two-phase fluid. Still, flow regime models used in thermal–hydraulic calculation codes rely heavily on earlier experimental data, featuring a narrow application range and unsatisfactory accuracy. To extend the prediction range and improve the accuracy of flow regime prediction by making full use of the increasing experimental data, integrated flow regime prediction models considering vertical flow direction for light water reactor (LWR) and horizontal flow direction for Canadian Natural Deuterium Uranium (CANDU) were developed and evaluated based on three standard machine learning algorithms in this study. Firstly, the experimental data of horizontal and vertical flow collected from the literature was modified and preprocessed to be training data. Then, the multi-layer perceptron (MLP) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm were utilized to develop the flow regime prediction model respectively. The performance of the three models was evaluated and compared and the results showed that the flow regime prediction model based on RF was the optimal model with higher prediction and more efficiency than the other two models. Finally, the flow regime prediction model was compared with existing models in three directions respectively, which indicated that the range and accuracy of the model based on RF were superior to the existing models. This study provides an implantable and scalable approach for flow regime prediction, and the application range and accuracy of flow regime prediction can be continuously improved with updating training data. [ABSTRACT FROM AUTHOR]

Fuel pins assembled in Pressurized Water Reactor (PWR) and Liquid Metal-cooled Fast Reactor (LMFR) core are usually cooled by single-phase coolant such as water or liquid metal. Researches on the coolant flow and heat transfer phenomena through rod bundles is quite essential for the nuclear reactor core design. This paper briefly summaries the current developments of Computational Fluid Dynamics (CFD) studies on the thermal hydraulic analysis of single-phase flow in the rod bundles of nuclear reactor core. On account of the tremendous amount of publications related to each aspect above, this brief review would focus on the most representative researches in the past decades. The aim of this paper is to provide an outline and present a comprehensive overview of the CFD research and guidelines on single-phase flow through bare rod bundle rod with mixing vane spacer and wrapped wire spacer in reactor core. In addition, the analysis and validation methods for various CFD calculating methods are concluded. The applications of CFD approach with different type of rod bundles are summarized. Finally, some remaining challenges for future work are proposed and discussed. [ABSTRACT FROM AUTHOR]