A dataset of thermal striping at the reactor core outlet

BackgroundThermal striping caused by the mixing of coolant at different temperatures in the reactor core outlet region induces alternating thermal stresses on components and triggers high-cycle thermal fatigue. This represents a critical thermal-hydraulic issue affecting reactor structural integrity. Currently, thermal striping data for core outlets are scattered across literature, lacking unified organization and standardized description, which hinders data sharing and in-depth utilization.PurposeThis study aims to integrate and standardize existing thermal striping data existed in various literature.MethodsA dataset suitable for multi-working-fluid comparisons, model validation, and data-driven analysis was established in this study, thereby supporting reactor thermal-hydraulic safety design and mechanism research.Experimental and large eddy simulation data from over 40 published papers were systematically collected, covering three working fluids (water, liquid sodium, and liquid lead-bismuth) and typical simplified geometries such as parallel multiple jets, coaxial jets, and fuel assembly heads. Then, thermal striping characteristic curves underwent triple-repeat extraction and verification using graphical digitization extraction. Finally, data were standardized through unit conversion, characteristic length normalization, and dimensionless processing.ResultsThe dataset spans operating conditions including temperature differences of 298~833 K, flow velocities of 0.2~3.36 m∙s-1, and momentum ratios of 0.33~1.5. It includes characteristic parameters such as mean temperature, thermal striping intensity, and power spectral density. Data extraction consistency error is less than 1.0%, ensuring high reliability.ConclusionsThis dataset transforms fragmented core outlet thermal striping data into standardized, traceable, and comparable resources. It supports cross-comparisons of thermal striping mechanisms across different working fluids, validation and assessment of turbulent heat transfer, and data-driven modeling targeting metrics like temperature fluctuation intensity and characteristic frequency. It provides essential data support for thermal-hydraulic safety analysis and design optimization of reactors.