Under prolonged high-temperature and high-pressure water chemistry conditions, chalk river unidentified deposits (CRUD) readily form on the surfaces of pressurized water reactor (PWR) fuel rods, posing a potential threat to the cladding's thermal-hydraulic safety. In this study, a CRUD growth model is developed by considering corrosion product transport, crystallization, deposition, and erosion, and is coupled with a deposition thermal resistance model to evaluate the effect of CRUD on rod bundle heat transfer. The thermal resistance model is validated against experimental data from the WALT loop. Based on this validation, a coupled simulation of CRUD growth and cladding thermal performance is performed for a 3×3 PWR fuel assembly subchannel. Results show that CRUD thickness is much higher in subcooled boiling regions due to enhanced particle deposition. After 100, 300, and 500 days, the average CRUD thicknesses are 3.08 μm, 8.67 μm, and 13.57 μm, while the corresponding average cladding temperature rises are 0.68 K, 1.59 K, and 2.44 K. As the deposition layer grows, cladding temperature continues to increase; when thickness reaches 54.07 μm, the maximum temperature rise reaches 10.81 K. These findings provide useful insights for predicting CRUD deposition and improving thermal-hydraulic safety in PWRs.

The safe operation of steam generators (SGs) is vital for the reliability of nuclear power systems, as they serve as the primary boundary for heat transfer. However, most available thermal-hydraulic analysis codes simplify the interaction between the two sides and do not fully capture the three-dimensional distribution of coupled heat source terms. To address this, we developed STAF 3.0, a full 3D thermal-hydraulic analysis code based on the OpenFOAM platform. The code's capability in modeling SG 3D flow fields was validated against scaled-down experimental data from the CNPOTC. Using a typical Gen-III 1000 MWe-class PWR SG as the research subject, we performed simulations under two characteristic design conditions. Subsequently, we carried out full-scale, non-linear constrained turbulent buffeting analyses on selected heat transfer tubes. The findings demonstrate that the asymmetric structure of the SG hot leg induces significant non-uniformity in primary side flow, subsequently impacting the temperature distribution across both sides and the secondary side void fraction. Specifically, the maximum primary-side temperature difference between symmetric positions in regions P1 and P2 was 2.22 K, while the secondary-side temperature and void fraction differences reached peak values of 2.06 K and 0.056, respectively. The outermost heat transfer tube exhibited the highest lateral impact stress, peaking at 5027 J m−3. Furthermore, the maximum in-plane and out-of-plane RMS amplitudes, at 0.141 mm and 0.202 mm respectively, were observed in the straight tube section.

Uneven coolant mixing in pressurized water reactors (PWRs) may produce non-uniform boron distributions, leading to local power distortions and reduced safety margins. This study develops a Python-based coupling method between the open-source CFD code OpenFOAM and a system analysis code. The framework employs domain decomposition and explicit time coupling to enable cross-platform data exchange. A turbulent boron transport solver is further developed from pimpleFoam for bidirectional transfer of flow and concentration data at coupling interfaces. The method is validated against a double T-junction tracer-mixing experiment and the ROCOM-STAT-01 benchmark. For the double T-junction case, predicted normalized tracer concentrations at WM1, WM2, and WM3 agree with measurements within ± 15 %. For ROCOM-STAT-01, the area-averaged mixing scalars at four representative locations remain within ± 10 % of the experimental values during the quasi-steady period. The validated framework is then applied to investigate coolant flow and boron diffusion in the reactor pressure vessel under different symmetric four-loop flow-rate levels. Results show pronounced circumferential and radial flow non-uniformities and strong coupling between boron diffusion and the local flow field. A recirculation zone near the 45° azimuth in the upper downcomer causes radial concentration non-uniformity, while the core inlet exhibits higher boron concentration near the periphery and lower concentration near the center. Varying all four loop flow rates synchronously from 70 % to 120 % of nominal mainly changes boron diffusion rate and local mixing without altering the overall flow pattern. The proposed method provides a reliable multi-scale tool for PWR boron-mixing safety analysis.

This study investigates the behavior of fission products during cladding failure in lead-based nuclear reactors, with a particular focus on the release mechanisms of solid fission products. A three-dimensional numerical model of the cladding-breach-subchannel system was developed to analyze these processes. To account for the phenomenon of deposited particles re-entering the fluid from the surface, this study proposes a torque balance-based resuspension criterion specifically designed for vertical walls. This method enables an accurate simulation of particle resuspension behavior within the Lagrangian framework. The results reveal that the release of fission products occurs in two distinct stages: the rapid depressurization stage and the equilibrium release stage. During the rapid depressurization stage, a significant pressure differential between the interior and exterior of the fuel rod drives the formation of a high-speed jet, which transports a substantial quantity of solid fission products into the coolant. The resuspension and release of deposited particles predominantly take place in this stage. However, the final release rate of resuspended particles is significantly lower than that of migrating particles, indicating that particle resuspension is a critical factor in limiting the overall release of solid fission products.

The Helical Cruciform Fuel (HCF) has a large heat transfer area and is self-supporting, showing great potential for improving reactor efficiency and safety. However, there is still a lack of research on the melting and relocation behavior of the dispersion HCF under accident conditions. Based on the Incompressible Smoothed Particle Hydrodynamics (ISPH) method, this paper uses the Discrete Phase Model (DPM) and the Discrete Element Model (DEM) to solve the coupled motion of dispersion fuel particles and molten matrix, and further analyzes the dispersion fuel settling behavior in the melted matrix inside the cladding and the relocation behavior of matrix and dispersion fuel outside the cladding. The analysis results indicate that the settling behavior of fuel particles in the fin zone is influenced by the twisted structure, leading to local overheating of the cladding. After the failure of the cladding, the relocation states of the molten material in the cladding groove include the transition from droplet slip to stream flow, followed by the separation of small droplets, ultimately forming a narrow-wide-narrow blockage structure. This paper provides a theoretical basis and guidance for the safety assessment and accident mitigation strategy formulation of the dispersion HCF.

High-temperature heat pipes, renowned for their exceptional thermal conductivity and inherent safety, are extensively employed in applications such as solar receivers and small nuclear reactors. While most existing research evaluate their thermal performance under static conditions, which do not adequately represent dynamic operating environments. This study aims to examine the behavior of a horizontal sodium heat pipe under pitch and roll motions generated by a six-degree-of-freedom robotic arm, by measuring the wall temperature, vapor temperature, and vapor pressure. The results indicate that, at a heating power of 1245 W, the equivalent thermal resistance under static horizontal condition is 0.1349 K/W. Pitch motion leads to noticeable periodic oscillations in temperature and vapor pressure, with oscillation periods aligning with the motion period. Consequently, the thermal resistance varies from -9.49% to 4.30% compared to the static horizontal condition. However, no thermal oscillations were observed during roll motion, as the periodic force field did not influence the return flow of the working fluid. These findings suggest that, while high-temperature heat pipes maintain robust safety under swinging motion, the potential impact of stress oscillations on the long-term reliability of the heat pipe should still be considered. This study offers valuable insights for the design and safety assessment of high-temperature heat pipes in dynamic environments.

Sodium-based high-temperature heat pipes (HTHPs) exhibit great potential for aerospace thermal management and nuclear reactor cooling due to their high thermal conductivity and long service life. Their heat transfer performance is primarily constrained by the capillary characteristics of internal wick structures. This work systematically investigates the capillary characteristics and capillary limit of stainless steel screen mesh wicks for sodium-based HTHPs. A visual experimental system in a vacuum glove box is established to conduct sodium capillary suction tests under different structural and thermal conditions. The results show that capillary absorption initiates at 500 °C, and the liquid rising height increases with temperature. Capillary pressure is enhanced by higher mesh numbers but is nearly independent of mesh layers. High-temperature interfacial reactions improve wick permeability with increasing temperature, while permeability decreases with both rising mesh number and layer quantity. The capillary performance factor slightly increases with temperature but declines with more mesh layers, presenting temperature-dependent mesh number sensitivity: it increases monotonically with mesh number at 500 °C, while increasing first and then decreasing above 500 °C. The 1-layer 400-mesh wick achieves the optimal comprehensive capillary performance with a maximum factor of 6.984 × 10−7 m at 600 °C. Compared with the inaccurate self-developed theoretical model, the BP neural network realizes precise height prediction of capillary height with a correlation coefficient (R) of 0.9954. Furthermore, an optimized capillary limit prediction model is proposed, whose relative errors are reduced by 10.42% and 8.00% compared with existing models with improved prediction accuracy. This study provides fundamental data and reliable models for the design and optimization of sodium-based HTHPs.

High-order separated flow models used in nuclear thermal-hydraulic simulations often fail under complex transients. To enhance stability and engineering applicability, this study proposes a machine learning-based method for model performance evaluation and adaptive order adjustment. A random forest classifier, optimized by probability calibration and threshold adjustment, was trained using a dataset of M310 and HPR1000 reactors. Borderline-Synthetic Minority Oversampling Technique was used to address class imbalance and improve detection of unstable cases. The method achieved 95.64 % recall, 92.31 % precision, and 0.005 s average evaluation time. The final model, integrated as a C++ dynamic-link library, enables early warning of calculation failures within simulation environments. This approach significantly improves the robustness and intelligence of thermal-hydraulic simulations, filling a gap in automated failure prediction.

Dispersed fuel has the advantages of radiation resistance and good heat transfer performance, and is widely used in research reactors. Unlike traditional homogeneous fuels, the melting behavior of dispersed fuels is a coupling of multiple physical phenomena, including heat transfer between the dispersed fuel particles and the matrix, migration behavior of dispersed fuel particles by molten material, etc. Focusing on the melting failure process of dispersed fuel, a numerical simulation method was established based on Moving Particle Semi-implicit method and validated using the submerged landslide experiment and debris head heat transfer experiment. Then, the melting failure behavior of dispersed fuel was simulated dynamically, and the migration morphology data and mass flow rate of the molten material and solid dispersed particles were obtained. Compared with other continuous phase-dispersed phase two-phase coupling models for particle method, the advantages of the coupled heat transfers and motion model in our method mainly include: realizing the coupled calculation of important phenomena during the dispersed fuel melting failure process; the free setting of the sizes of dispersed phase particle and continuous phase particles; the continuous phase particles can interact with the internal dispersed phase particles when dispersed phase particle aggregate into clusters. This study conducts an in-depth analysis of the key phenomena and laws in the melting failure process of dispersed fuels, aiming to provide new ideas and methods for research in related fields.

The mechanisms of direct-contact boiling between subcooled water and high-temperature lead-bismuth eutectic (LBE) during the steam generator tube rupture (SGTR) remain partially unclear. This study experimentally investigated the direct-contact boiling process induced by water injection into a large-volume high-temperature LBE pool, focusing on the resulting dynamic pressure wave in LBE, pressure rise in gas, and temperature distribution. Eleven experimental cases were conducted under varying conditions of injection nozzle diameter (5-10 mm), LBE temperature (270-400 degrees C), secondary-side water pressure (3.1-5.8 MPa), secondary-side water temperature (28-85 degrees C), and gas-to-LBE volume ratio (2:3-3:2). Observations revealed non-uniform vapor cavity expansion, entrainment of subcooled water in gas cavities, and attenuation of dynamic pressure waves. The peak dynamic pressures at different measurement locations (PS1 and PS2) were observed in distinct peak segments. Specifically, the peak at PS1 occurred within the first segment, while that at PS2 appeared in the second or third, which suggested the presence of pressure wave superposition effects. Quantitative relationships were established through empirical correlations, which demonstrated that the LBE temperature, water temperature, and gas-to-LBE volume ratio are positively correlated with the LBE dynamic pressure peak, which ranged from 1.670 to 3.368 MPa. The derived impact indices for these three parameters in the power-law correlations are 0.99, 0.99, and 0.17, respectively. Injection nozzle diameter, LBE temperature, water pressure, and water temperature are positively correlated with the average pressurization rate (0.511-1.122 MPa/s) in the reaction vessel, while gas-to-LBE volume ratio exhibits a clear negative correlation. The findings contribute to clarifying the mechanisms of direct-contact interactions between subcooled water and high-temperature liquid metals.

The release of fission gas in nuclear fuel significantly impacts fuel performance. Currently, many engineering models for fission gas release (FGR) rely on empirical corrections of simplified processes, introducing considerable uncertainty. Therefore, implementing mechanism-based FGR models grounded in physical behavior is crucial for improving the reliability of fuel performance codes. In this study, an established mechanism-based FGR model (incorporating atomic diffusion, intra-granular bubble re-solution, grain-boundary sweeping, and inter-granular bubble dynamics) was integrated into the fuel performance analysis code FROBA, along with a non-thermal release model. The implementation couples grain-boundary gas release with swelling equations. Model validation against literature benchmarks under steady-state conditions demonstrates excellent agreement with experimental data and other codes for both FGR fraction and swelling rate. Uncertainty analysis confirms the model’s effectiveness within the implemented scope.

With the global emphasis on carbon neutrality, nuclear energy has become an essential part of green and low-carbon energy sources. Ensuring the safe operation of nuclear energy systems is a necessary prerequisite for the large-scale deployment of nuclear energy. However, traditional nuclear system analysis codes, which rely on semi-empirical correlations, are gradually unable to meet the accuracy requirements for the continued development of nuclear systems. This study addresses these limitations by incorporating machine learning (ML) algorithms into the analysis of nuclear energy systems and system safety analysis were conducted based on the system analysis code with ML. First, ML models were developed using experimental and simulation datasets to predict flow regimes, critical heat flux (CHF), and heat transfer coefficients (HTC), which are key parameters for calculating two-phase flow. These models were then integrated into a nuclear system analysis code, which was validated against experimental data from the Thermal-Hydraulic Test Facility (THTF) and the Loss-of-Fluid Test (LOFT), demonstrating high predictive accuracy. Finally, a nuclear energy system model for an offshore floating nuclear plant (OFNP) was established, and accident safety analyses for loss of flow accident (LOFA) and loss of coolant accident (LOCA) was conducted based on the code with ML to investigate the impact of various initiating events on accident consequences. The results show that the safety systems of OFNP can ensure safe operation under different accident conditions. In the case of LOFA, accident consequences were consistent across different numbers of power failure pumps, with the highest peak cladding temperature occurring under two pumps failure. In the case of LOCA, the accident consequences varied with break size, and the peak cladding temperature was highest under the 50 mm break condition. This research highlights the substantial potential of machine learning to improve nuclear safety assessments, offering a novel and practical approach to advancing the reliability of nuclear energy systems.

Molten salt reactors, characterized by circulating liquid fuel, exhibit neutronic and thermal-hydraulic behaviors that differ fundamentally from those of solid-fueled reactors. Accurately modeling the convection-driven transport of delayed neutron precursors and its impact on reactor reactivity remains a challenge for systemlevel analysis codes. Capturing the spatial distribution of decay heat in the primary loop is another key challenge. To overcome these limitations, a high-fidelity kinetics analysis module is developed based on the Reactor Excursion and Leak Analysis Program (RELAP5/MOD3.2) code. The one-dimensional delayed neutron precursor transport equation is solved using a third-order Runge-Kutta discontinuous Galerkin method, which effectively reduces numerical diffusion and improves spatial resolution in convection-dominated problems. In addition, a simplified decay heat model is established to compute the decay power distribution within the primary loop and is solved consistently with the delayed neutron precursor equation. The developed code is validated against startup, coastdown, and reactivity insertion experiments from the Molten Salt Reactor Experiment. The results show good agreement with experimental measurements and accurately predict delayed neutron precursor migration and transient power behavior. Further analysis of the Molten Salt Breeding Reactor based on the validated model indicates weak negative feedback and high sensitivity to flow perturbations. Under an unprotected loss of heat sink accident, the reactor shows a slow shutdown response. These results demonstrate that the proposed approach provides a reliable tool for the safety analysis of molten salt reactors.

Spray cooling technology, known for its high heat transfer efficiency, is widely applied in high heat flux scenarios. However, existing studies often lack efficient model transition strategies for simulating droplet impingement, liquid film formation, and evaporative heat and mass transfer processes, resulting in high computational costs and limited applicability to large-scale fields. This study proposes an innovative numerical spray cooling method called DPM-VOF-LEE. It integrates Volume of Fluid (VOF) and Discrete Phase Model (DPM) with an evaporative heat transfer model through a transition strategy. The DPM model is employed for efficient droplet tracking in the far-field region. In contrast, the VOF model is applied near the wall to resolve liquid film morphology and heat transfer accurately. This model transition method significantly reduces mesh requirements and improves scalability. It is especially suitable for large-area or multi-nozzle spray cooling systems. Results indicate that vertical single-nozzle spraying exhibits the best cooling performance. In dual-nozzle configurations, interference regions enhance heat transfer. Cooling efficiency increases by more than 78 % compared with non-interference cases. For triple-nozzle configurations, the staggered layout achieves faster average temperature reduction on aluminum plates, with cooling efficiency 8.32 % higher than the inline layout.

Filtration is important for impurity control and hydraulic reliability in liquid-metal cooling systems. However, the coupled evolution of particle transport, clogging morphology, and pressure-drop growth in liquid-metal filters remains insufficiently resolved. In this study, a coupled macroscopic particle model and discrete element method (MPM–DEM) framework is developed to investigate particle retention and clogging behavior in a lead–bismuth eutectic (LBE) filter channel. The model is validated against pressure-drop correlations for packed particle beds and shows reasonable agreement with theoretical predictions. For a clean filter, the simulated pressure drop is 4483.63 Pa at an inlet velocity of 0.2 m/s, giving a relative error of 11.67% compared with the theoretical value. The results indicate that filtration evolves through two successive stages, namely deep filtration and surface filtration. During deep filtration, partial pore blockage leads to limited and fluctuating pressure-drop growth, whereas after the clogging point a stable filter cake is formed and the pressure drop increases approximately linearly with the mass of retained particles. Increasing the mean particle diameter markedly delays the onset of clogging, while broadening the particle size distribution produces a weaker but similar effect. A hydraulically equivalent porosity is further introduced to characterize the effective permeability of the clogged cake and to relate deposit structure to pressure-drop evolution. The proposed framework provides insight into clogging-induced hydraulic degradation and offers a basis for pressure-drop prediction and filtration performance assessment in liquid-metal cooling systems.

In recent years, small modular reactors, particularly heat pipe reactors, have garnered considerable interest owing to their miniaturization, modular design, and inherent safety characteristics. Extensive research efforts worldwide have yielded numerous conceptual designs. Meanwhile, space vehicles such as spacecraft and space stations demand reliable energy systems with stable power output, high efficiency, and long operational lifespans. Heat pipe reactors are well-suited for such applications. This study presents a conceptual design of heat pipe cooled reactor using in space environment, termed the Sodium (Na)-based Space Thermal-Electrical Reactor (Na-STER). The system comprises five main modules: the reactor and shielding module, thermoelectric conversion module, energy transmission module, waste heat removal module, and auxiliary module. The reactor is designed to deliver an electrical output of 240.2 kW with an energy conversion efficiency of 20.02 %. Comprehensive neutronic and thermal analyses are conducted. Results show axial and radial power peaking factors of 1.33 and 1.37, respectively, indicating a relatively uniform core power distribution. The maximum core temperature reaches 1061.1 K under full-power conditions. Furthermore, the reactor's response to selected accident scenarios is assessed, demonstrating favorable inherent safety characteristics. This work provides valuable insights and design references for future nuclear power systems employing heat pipe reactor technology in space environments.

Natural circulation is a vital technique to carry out residual heat in reactors under severe accident. Nowadays passive secure design is considered as a basic feature of advanced reactors. So that natural circulation has become more promising for getting rid of main pump, especially in Floating Nuclear Power Plant (FNPP) design. Previous study on natural circulation characteristics mainly focused on single circulation loop and low temperature or pressure conditions. In this study, experimental study on single phase natural circulation characteristics of symmetric loop under high temperature and high pressure as well as motion conditions was conducted, influence of different motions on system natural circulation was analyzed. Results showed that for the design in this experiment, all motion conditions would weaken the natural circulation driving force in different extent, while in them pitching would work most significantly. From another perspective, under pitching conditions, motion additional force worked together with gravity and buoyance force, enhancing the turbulent mixing of fluid, so that heat transfer coefficient was raised under motion conditions.

Reliable and autonomous operation is essential for advanced gas-cooled reactor-Brayton energy systems operating under highly dynamic and uncertain conditions. This study develops an integrated computational framework that couples high-fidelity thermal-hydraulic simulations with data-driven surrogate modeling and intelligent fault management. The proposed framework, termed the Space Advanced Reactor with Gas-cooled Energy conversion system (SARGE), incorporates four synergistic modules for transient analysis, surrogate prediction, fault identification, and expert-rule intervention. Surrogate models based on gated recurrent units accurately reproduce ten key system response variables with mean absolute percentage errors below 1.1 %, achieving a computational acceleration exceeding 4651-fold compared with full physics-based simulations. A hybrid convolutional-bidirectional long short-term memory network combines disturbance parameters with surrogate-predicted responses to classify eight representative fault scenarios, yielding 96.50 % precision and 96.50 % recall while maintaining perfect recognition for previously unseen cases. Expert-rule interventions further constrain abnormal excursions and restore nominal operating states, thereby enhancing system resilience. The proposed surrogate-intelligence synergy establishes a unified Disturbance-Prediction-Identification-Intervention paradigm for real-time monitoring and adaptive control, offering a transferable approach to next-generation intelligent thermal energy systems and high-efficiency reactor applications.

In the event of a severe accident at a nuclear power plant, many radioactive aerosols will be dispersed in the containment. Aerosols can leak into the environment through tiny gaps in the containment vessel. In the early nuclear safety analysis, researchers often ignore the retention of aerosol particles in the tiny channels, resulting in an overly conservative analysis of radioactive source terms. It has been shown that aerosol deposition occurs due to various mechanisms, which have a significant removal effect on aerosol particles. In this study, numerical simulation has been used to study the aerosol deposition patterns and laws in the micro-channel. The effects of particle deposition time, aerosol particle size, and Reynolds number of carrier gas on the deposition rate of aerosol particles are analyzed based on OpenFOAM. The results show that: The particle deposition rate increases as the particle size increases. As the Reynolds number of the carrier gas increases, the particle deposition rate decreases. When the Reynolds number increases from 200 to 7500, the total particle deposition rate drops from 94.3 to 14.8

Flow-induced solidification of high-melting-point liquid metals in cooled passages can reduce the effective flow area, increase hydraulic resistance, and deteriorate heat transfer performance. To quantify these coupled effects, a three-dimensional solidification model was developed and validated against experimental data; calculated outlet temperatures agree with measurements within +/- 4%. Using the validated model, 147 operating conditions (99 for correlation calibration and 48 for validation) were simulated to quantify the dependence of steady-state blockage ratio (VOR), extra pressure-drop parameter (Psi) and wall-solid heat-transfer behavior on inlet boundary conditions. An empirical correlation predicting VOR from five boundary parameters was obtained; the fitted correlation shows <= 12% deviation from the calibration dataset and <= 16% deviation for the validation dataset. Results indicate a near-linear dependence of Psi on VOR for fixed liquid-metal Reynolds number and a monotonic dependence of the associated deterioration coefficient on Re. The wall-solid heat-transfer coefficient decreases with increasing solidified-layer thickness and increases with liquid-metal inlet velocity and temperature. These results provide a quantitative basis for rapid prediction of blockage severity and thermohydraulic deterioration in liquid-metal forced convection systems.