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Summary: Supercritical heat transfer loops are widely used in large‐scale thermal equipment, which development is restricted by the thermal‐hydraulic characteristics, especially the safety and thermal efficiency under high‐heat fluxes. This study is focused on numerical simulation for a 4‐m test section of the loop and further analyzes the flow transitions and heat transfer mechanisms from subcritical to supercritical. The heat transfer characteristics of supercritical water in the vertical loop were simulated for conditions: P = 24 MPa, Tin = 320°C, 340°C, mass flux (G) = 1000 kg/m2s, 1500 kg/m2s, and heat fluxes varied from 100 to 1200 kW/m2. The heat transfer transitions are analyzed from the perspective of local parameters that affect the status of both core flow and boundary layers. The empirical correlation and the error backpropagation (BP) neural network prediction model are proposed and compared with experimental and numerical results. The results show that transitions of both boundary layer and core flow from subcritical to supercritical contribute to the heat transfer behaviors: normal heat transfer, heat transfer deterioration, and heat transfer enhancement. The zero‐velocity gradient (∂u/∂r = 0) on the M‐shaped velocity curve causes the turbulent shear stress near the wall to be close to zero, which leads to the suppression of the turbulence. At the same time, the thickness of the thermal boundary layer reaches the maximum, resulting in a large thermal resistance, which affects the heat transfer from the wall to the core area. [ABSTRACT FROM AUTHOR]

The traditional process capability indices Cp/Cpk for evaluating process capability used in the current industry are based on the probability model of a process distribution, which makes it difficult for many processes whose actual output with non-normal distribution to apply these indices. In this paper, new process quality indices (PQI) Qp/Qpk are proposed for measuring the quality capability of the process. The new PQI are derived from Taguchi's quality loss function model and do not need to estimate the probability distribution of the process output. The traditional process capability indices Cp/Cpk are zero-defect indices, it focus on meeting the process specification requirements, while the new PQI Qp/Qpk are zero-loss indices, it focus on approaching the process target value. The construction of the new PQI is logical, simple, and practical. The PQI approach is more suitable for quality management in the Era of big data. [ABSTRACT FROM AUTHOR]