Because of their high molecular heat conductivity, low-Prandtl number liquid metal is a promising candidate coolant for various designs of advanced nuclear systems such as liquid metal–cooled fast reactors and accelerator-driven sub-critical system (ADS). With the fast-growing computational capacity, more and more attention has been paid to applying computational fluid dynamics (CFD) methods in thermal design and safety assessment of such systems for a detailed analysis of three-dimensional thermal–hydraulic behaviors. However, numerical modeling of turbulent heat transfer for low-Prandtl number liquid metal remains a challenging task. Numerical approaches such as wall-resolved large eddy simulation (LES) or direct numerical simulation (DNS), which can provide detailed insight into the physics of the liquid metal flow and the associated heat transfer, were widely applied to investigate the turbulent heat transfer phenomenon. However, these approaches suffer from the enormous computational consumption and are hence limited only to simple geometrical configurations with low to moderate Reynolds numbers. The Reynolds-averaged Navier–Stokes (RANS) approach associated with a turbulent Prandtl number Prt accounting for the turbulent heat flux based on Reynolds analogy is still, at least in the current state in most of the circumstances, the only feasible approach for practical engineering applications. However, the conventional choice of Prt in the order of 0.9∼unity in many commercial computational fluid dynamics codes is not valid for the low-Prandtl number liquid metal. In this study, LES/DNS simulation results of a simple forced turbulent channel flow up to a friction Reynolds number Reτ of 2000 at Pr of 0.01 and 0.025 were used as references, to which the Reynolds-averaged Navier–Stokes approach with varying Prt was compared. It was found that the appropriate Prt for the RANS approach decreases with bulk Peclet number Peb and approaches a constant value of 1.5 when Peb becomes larger than 2000. Based on this calibrated relation with Peb, a new model for Prt used in the RANS approach was proposed. Validation of the proposed model was carried out with available LES/DNS results on the local temperature profile in the concentric annulus and bare rod bundle, as well as with experimental correlations on the Nusselt number in a circular tube and bare rod bundle.

Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structures to improve system safety and reliability. However, reliability of the actual system is often lower than that obtained by theoretical calculation due to the inevitable occurrence of common mode fault (CMF), which is a dependent failure event that can cause multiple failures in redundant channels. CMF may increase the reliability deviation of the system by orders of magnitude and, hence, seriously affects the reliability of the system. To mitigate the CMF of redundant sensors in nuclear power plants, an artificial neural network (ANN) can serve as a data-driven analytic model to monitor sensor parameters, to identify any possible abnormal status of the sensors, and provide an early warning. In this study, by using the high-fidelity dataset obtained in a full-scope PWR simulator as training, validation, and test data, a relevant parameter-based ANN black-box model (RPANN) was established by employing the back-propagation (BP) learning algorithm, which was then defined as an analytic redundancy. Time series-based ANN checking models (TSANNs) were also established for each of the input and output parameters of the RPANN in order to identify its abnormal state based on historical data in the past. When combined with the existing hardware redundancy, the ANN-based analytic redundancy can serve as an online monitoring tool of the hardware status and an online diagnosis strategy for sensor faults. Furthermore, ANN-based analytic redundancy can replace faulty hardware sensors to analytically reconstruct the reading of the monitored sensor parameter without having to reduce the reactor output power or even shut down the reactor for emergency maintenance so that the on-site calibration frequency of hardware sensors in redundant channels can be effectively reduced. This is not only of vital importance in reducing operation and maintenance costs of existing PWR power plants but also plays an important role in building reactor operation schemes with rapid and frequent changes in power output in the future. Simultaneously, the diverse redundancy combining analytic software redundancy and physical hardware redundancy can effectively reduce the threat of CMF of hardware sensors on the operation safety of reactor systems.

During the refueling process of lead–bismuth eutectic (LBE)–cooled fast reactors, lead–bismuth alloy can easily adhere to the surface of the fuel rod clads when spent fuel assemblies are unloaded from the reactor core. For some designs, the lead–bismuth alloy attached on the spent fuel rods will be cleaned via physical or/and chemical methods prior to their transportation, storage, and reprocessing. In this article, the cleaning effect of a washing lotion composed of hydrogen peroxide (H₂O₂) aqueous solution and concentrated acetic acid (CH₃COOH) as the main components on LBE was experimentally investigated. By adjusting the composition ratio of the washing lotion and the reaction temperature, the law of their influence on the cleaning effect of LBE was determined. The optimal washing lotion composed of 30 Vol% hydrogen peroxide aqueous solution as oxidant, 40 Vol% concentrated acetic acid as acid, and 30 Vol% ultrapure water as the solvent was proposed based on experimental investigations. The optimal working temperature range was also obtained. The reaction intermediate product was characterized with the XRD analysis in order to understand the reaction mechanism. The composition of the released gas (even in a slight amount) was also analyzed with gas chromatography. The hydrogen concentration in the released gas was found to be lower than the detection limit of gas chromatography (10 ppm). Since the explosion limits of hydrogen at atmospheric pressure are between 3.95 and 75.73 Vol%, the risk of possible hydrogen explosion is concluded to be extremely low. Therefore, no special treatment of the released gas is required even in a large-scale industrial platform. Furthermore, corrosion effect of the washing lotion on austenitic stainless steel components was tested as well and found to be negligibly small. The relative mass loss of the thin-walled stainless steel samples immersed in the washing lotion was found to be less than 0.5% after an experimental duration of 144 h, which facilitates sufficient cleaning time in future industrial applications.