The insertion of molybdenum (Mo) disks into UO2 pellets is anticipated to improve heat transfer efficiency, potentially enhancing the in-pile performance of nuclear fuel rods. To analyze the performance of Mo disk enhanced UO2-Zr fuel rods, a numerical heat transfer model was developed in this paper. Building upon the fuel performance analysis software FROBA, the heat transfer module was enhanced through the implementation of localized two-dimensional (2D) heat conduction calculations for the Mo disk and fuel pellet. This refinement is integrated with the existing one-dimensional (1D) methodology employed for full-length fuel rod analysis. The accuracy of the model and software updates was validated through comparisons with COMSOL finite-element simulations and the original version of FROBA. Using the updated FROBA software, the performance of the Mo disk enhanced UO2-Zr fuel rod was analyzed: (1) The inclusion of 5% Mo disks lowers the fuel peak temperature by 692 K, decreases the end-of-life (EOL) fission gas release (FGR) fraction from 38.4% to 10.6%, and reduces the internal pressure from 15.8 MPa to 7.1 MPa. (2) As the Mo volume fraction increases from 2% to 5% and further to 8%, the fuel temperature decreases by 79 K and 112 K, respectively. However, the incremental temperature reduction diminishes at higher Mo volume fractions. (3) When the fuel pellet height is reduced to one-quarter of its original dimension, the maximum operating temperature of the fuel rod decreases by 707 K, while the FGR drops from 27.3% to 2.9%.

Lead-bismuth eutectic (LBE) cooled reactors have attracted significant attention due to their excellent neutron economy and high heat transfer capability. However, during long-term operation, corrosion products and impurity particles generated in the LBE coolant may deposit on steam generator (SG) surfaces, forming a deposition layer that increases thermal resistance and degrades heat transfer performance. This study investigates the coupled effects of impurity deposition and thermal-hydraulic behavior in the LBE/water single-pass counter-current steam generator of the MYRRHA reactor.A comprehensive numerical method is developed, integrating particle migration mechanics, critical velocity deposition criteria, and shear-induced removal mechanisms within the Eulerian-Lagrangian framework. The deposition layer feedback is modeled through equivalent thermal conductivity corrections and wall roughness modifications. Transient simulations are performed over an equivalent operational period of approximately three years, with particle mass concentration, deposition thickness distribution, and resulting thermal performance changes analyzed in detail. The results indicate that impurity deposition exhibits strong spatial heterogeneity within the tube bundle. The most severe deposition occurs in the intermediate region of the bundle, where the average thickness reaches approximately 250 μm. The non-uniform deposition leads to an increase of about 5.1 K in the average outlet temperature of the primary side LBE, while the local tube wall temperature near the inlet can rise by up to 25 K. Correspondingly, the average Nusselt number decreases by up to 13.8% for outer tubes but increases by approximately 10.5% for certain central tubes. This study provides quantitative insights into the coupled deposition-thermal-hydraulics phenomena in LBE steam generators, offering a theoretical basis for long-term performance assessment and deposition management strategies in advanced nuclear reactor systems.

With the deepening understanding of turbulent heat transfer phenomena in liquid metals, neither purely experimental measurements or purely numerical simulations alone can meet the demands of current research. In recent years, the development and application of data assimilation technology can contribute to solve this problem. In this work, a data-model fusion-driven method was created based on the Ensemble Kalman filter (EnKF). Then it was applied to the calibration of turbulent Prandtl number (Prt) and the prediction of average Nusselt number (Nu) for the flow and heat transfer phenomena of lead-bismuth eutectic (LBE). Using the calibrated Prt model, the prediction of the average Nu not only achieved high agreement with Johnson's experimental data, with a relative error within 5%, but also significantly outperformed existing correlation formulas. Although its prediction accuracy decreases when extrapolating at low Peclet number (Pe), the error remains within +/- 15%. Under the higher Pe number condition, a single measurement point suffices for the calibration requirement while under the lower Pe condition, at least two measurement points are required to ensure reliable correction.

To meet the thermal management requirements of high-power spacecraft, a high-efficiency heat pipe radiator is designed, fabricated, and experimentally verified through an infrared (IR) cage thermal balance test. A thermal balance test is conducted to investigate the radiator's thermal balance characteristics under different orbital conditions, pumping flow rates, and external heat flux variations. Multiple infrared heat flux loading schemes are designed to represent steady-state and transient operating conditions. The results indicate that the IR cage can accurately reproduce the on-orbit thermal environment of the radiator. At a beta angle of 0 degrees, the external heat flux exhibits strong periodic variations, while at a beta angle of 83.5 degrees, the heat flux remains stable and evenly distributed. Increasing the pumping flow rate significantly reduces the average radiator temperature and enhances overall heat transport capability. An auxiliary heating power of approximately 390 W is required at a beta angle of 0 degrees and 10% flow to prevent fluid freezing. Multi-step infrared heat flux loading schemes effectively reproduce the transient variations of orbital heat flux. These findings provide essential experimental evidence and technical guidance for the design, ground verification, and in-orbit application of high-power spacecraft heat pipe radiators.

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-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.

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.

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.

The space lithium-cooled fast reactor (LFR) system incorporating the Stirling conversion power system is composed of reactor core, electromagnetic pump, gas-liquid separator, Stirling engine and heat pipe radiator. In this study, the lithium loop of the 2.4-MW space LFR was modeled based on RESYS code. Both full power and standby steady-state conditions are simulated to validate the accuracy and applicability of modeling the lithium loop. Three types of typical transient analyses are carried out, including the over-power accident, the low-power accidents, and the loss of flow accident (LOFA) in the lithium loop. The results provide valuable references for the shutdown signal setting, the operating temperature range of Stirling engine and the ground experiment for the space LFR system coupled Stirling engine.

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.

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.

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.

Critical heat flux (CHF) is a key thermal limit for boiling heat transfer and reactor safety, but data-driven CHF models often suffer from non-physical extrapolation near boiling crisis boundaries. In this study, a unit-aware physical embedding Transformer is proposed for CHF prediction. The model integrates learnable unit embeddings, unit-interaction features, multi-scale extraction and physics-informed regularization to preserve the physical semantics and scale consistency of thermal-hydraulic variables. The model was evaluated on 24,579 samples from the public CHF tube database under full-range testing and two extrapolation scenarios: highpressure holdout and high-quality holdout. In the full-range test, the proposed model achieved a root mean squared error (RMSE) of 213.91 kW m- 2 and a coefficient of determination (R2) of 0.9818. Under extrapolation conditions, the proposed framework maintained stable performance in high-pressure regimes and achieved the best overall accuracy in the high-outlet-quality dryout-dominated regime, with an RMSE of 327.87 kW m- 2 and an R2 of 0.7850. Unit-perturbation tests further showed that the model reduced systematic prediction drift by more than 60%. Interpretability analyses indicated physically consistent responses associated with mass flux, pressure and outlet quality. These results demonstrate that embedding unit semantics and physical constraints into Transformer-based CHF modeling can improve the robustness and credibility of thermal-limit prediction under extrapolative boiling crisis conditions.

Preventing radioactive leakage following a Core Disruptive Accident (CDA) is crucial for protecting the environment and human health. To mitigate such consequences, understanding in-reactor behavior is essential. In this study, a two-dimensional, three-velocity-field and multicomponent simulation code, ACENA, developed at XJTU, is employed to analyze CDA scenarios in the STAR-LM design. Steady-state results are first validated against design parameters to ensure model accuracy, and a hypothetical transient is then introduced to trigger CDA onset. The coupling between damaged core materials and coolant thermal–hydraulic fields is investigated, with particular focus on the motion and distribution of fuel particles with different densities. Results show that low-density fuel particles do not float to the coolant surface but accumulate at the interface between high- and low-temperature regions due to temperature-dependent coolant density variations. Various accident scenarios are further examined to assess material behavior and potential threats to reactor integrity. It is found that local Fuel Blockage Accidents (FBA) cause limited core degradation owing to structural confinement and sufficient cooling when the Primary Heat Exchanger (PHX) remains active, though transient boiling may occur. The study demonstrates ACENA’s capability for detailed CDA analysis in Liquid Metal Fast Reactors (LMFRs).

Aqueous Homogeneous Reactors (AHR) are used for the production of medical isotopes and are characterized by high economy and safety. Due to the closely packed multiple cooling coils in the core, the thermal–hydraulic analysis of AHR is grid-heavy and computationally time-consuming. Firstly, in order to reduce computational consumption, a coarse-mesh method is employed to model the core and cooling coil based on GeN-Foam. For the core, a Eulerian-Eulerian two-phase flow model with implanted fission gas mass source term and fission deposition energy source term is used. For the cooling coil, solution matrices for tube wall and cooling water are developed and constructed based on the finite volume method. Secondly, in order to improve the computation speed, the parallel computation of cooling coils is developed with GeN-Foam parallel framework. Thirdly, the developed code was applied to carry out a 3D thermal–hydraulic analysis of the MIPR core. The results show that the code is able to calculate the fission gas and energy deposition, and the core has reasonable temperature and void fraction distribution; the code is able to correctly identify and build the virtual mesh of the cooling coil, and the trends of the tube wall and cooling water temperature changes are consistent with the physical processes. Moreover, the speed of computation can be significantly increased by optimizing numerical algorithms and parallel strategies. This study can provide a reference for the three-dimensional thermal–hydraulic calculation of AHR.