This work establishes a dynamic model of an agile microreactor system (AMS), in which a heat pipe reactor is integrated with a SCO2 Brayton cycle, based on the MATLAB/Simulink platform. On this basis, two control strategies are considered, with one maintaining a constant turbine inlet mass flow rate and the other maintaining a constant turbine inlet temperature. To evaluate their performance, transient simulations are carried out for a load reduction from 100%FP to 90%FP. The results indicate that, when the turbine inlet mass flow rate is maintained constant, the turbine inlet temperature decreases from 873.3 K to 823.8 K, and the cycle efficiency is reduced to 27.05%, with a relatively long stabilization time of about 1900 s. In contrast, under the constant turbine inlet temperature strategy, the mass flow rate decreases from 16.5 kg/s to 16.02 kg/s, while the cycle efficiency reaches 27.13%, and the system can be stabilized within approximately 700 s. Overall, the control strategy with constant turbine inlet temperature demonstrates a faster response of key system parameters and a shorter stabilization time. This advantage makes it a more favorable option for control system development in advanced nuclear energy systems. These could provide technical support for the engineering application of AMS in the future.

The growing demand for new energy systems underscores the potential of heat-pipe-cooled microreactor (HPR), an advanced class of small modular reactors renowned for their high energy density, long operational lifespan, and superior environmental adaptability. A key challenge in their design is the strong nonlinear coupling between neutronics and thermal, arising from intense temperature feedback and non-uniform heat removal. This paper introduces a coupled neutronics and thermal model based on the Finite Element Method (FEM) to address challenges in power distribution and temperature variation during HPR design.This model is developed following the conventional “two-step” approach in reactor physics. First, group-averaged neutron cross-sections are generated using the Monte Carlo code OpenMC. These cross-sections are then functionalized. Crucially, the Super-Homogenisation (SPH) correction method is applied to preserve reaction rates in the homogenized model, which is essential for accurate power distribution. Using these processed data, a neutronic-thermal analysis is performed with COMSOL Multiphysics, establishing the coupled physics-thermal framework. The neutronics module of this model, which incorporates the SPH methodology, was validated against the IAEA 2D PWR benchmark problem, demonstrating its capability to accurately calculate the reactor’s effective multiplication factor and power distribution. Furthermore, the complete coupled model was applied to investigate reactor transients under various operating conditions.The results demonstrate that the proposed method reduces computational complexity without compromising accuracy, thereby providing a reliable theoretical and technical foundation for the design and optimization of HPR .

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.

The integrated severe accident analysis program constitutes a cornerstone of nuclear reactor safety, functioning as a pivotal tool for predicting accident sequences, diagnosing critical failure nodes, and formulating evidence-based management strategies throughout the accident progression. It enables the simulation of extreme scenarios-ranging from core overheating to containment breach-without exposing operational facilities to risk, thereby playing an irreplaceable role in optimizing safety protocols and enhancing emergency response capabilities. However, the landscape of global nuclear technology collaboration shifted profoundly with the imposition of U.S.-China trade restrictions, which explicitly prohibited the transfer of advanced nuclear safety analysis software to China. This move disrupted existing technological dependencies and posed a critical challenge to China's nuclear sector, as access to such software is indispensable for conducting independent safety assessments, validating novel reactor designs, and ensuring compliance with international safety benchmarks. The restriction underscored the urgency of technological self-reliance, highlighting that reliance on foreign tools could compromise both the development trajectory and security autonomy of China's nuclear industry. To address this gap, the Nuclear Thermal-hydraulic Laboratory at Xi'an Jiaotong University leveraged nearly two decades of cumulative expertise in nuclear thermal-hydraulics, severe accident phenomenology, and computational modeling to develop a domestically tailored solution. The research team overcame formidable challenges in simulating complex physical phenomena-including core melting, molten material interactions with structural components, and radioactive material release-each requiring rigorous mathematical modeling and validation against empirical data. The outcome of this endeavor is MOSAP (MOdular Severe Accident Analysis Program), a groundbreaking software specifically engineered for pressurized water reactors (PWRs), which dominate China's operational nuclear fleet. Endowed with complete intellectual property rights, MOSAP's modular architecture enables high-fidelity simulation of full severe accident sequences, encompassing initiating events such as Station Blackout (SBO) and Small-Break Loss-of-Coolant Accidents (SBLOCA), as well as subsequent phases: core degradation and melting, redistribution of molten corium within the reactor pressure vessel's lower head, vessel wall melt-through, and containment-level phenomena (e.g., hydrogen combustion, radioactive fission product transport). MOSAP has ushered in a new era of technological autonomy for China's nuclear industry. It empowers nuclear power plants to conduct quantitative assessments of accident probabilities and consequences, facilitating targeted preventive measures. For instance, operators can simulate the efficacy of emergency core cooling systems under extreme conditions or evaluate containment structural integrity during postulated melt-through scenarios, thereby optimizing safety protocols. Additionally, the program supports the development of nuclear simulators for operator training, ensuring proficiency in responding to emergency scenarios. Beyond its immediate applications, MOSAP represents a milestone in China's pursuit of self-reliance in high-end nuclear technology. It eliminates dependence on foreign software, positions China as a competitive player in global nuclear safety research, and strengthens its capacity to contribute to international collaborative initiatives-all while safeguarding the sustainable and secure development of its nuclear energy sector, a critical pillar of the global low-carbon transition.

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

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).

Lead-Bismuth Eutectic Cooled Fast Reactor (LBEFR) uses low melting point and high boiling point lead-bismuth as the coolant, and is one of the most promising Generation IV nuclear systems. However, fuel rods in LBEFR have to be subjected to more severe operation conditions compared with Light Water Reactor (LWR) fuels, such as harder neutron spectrum, higher temperature, and enhanced coolant corrosiveness, resulting in distinct evolution processes of fuel behavior. In this study, with full consideration of geometry and material design characteristics, models on distinct phenomena of LBEFR fuels, such as fuel constituent migration, pellet restructuring, cladding corrosion, have been developed and implanted into the program FROBA initially developed for LWR fuel analysis. The capability of the upgraded FROBA for LBEFR fuel performance analysis was preliminarily verified with several benchmark cases. Subsequently, a fuel behavior simulation was conducted for MOX-T91 fuel in a compact LBEFR under long-term normal operation and a power ramp following the normal operation. Compared with traditional LWR fuels, the LBEFR fuel operated at significantly higher temperatures, triggering substantially more fission gas release. To accommodate the large volume of released fission gases and suppress internal pressure rise, LBEFR fuel requires a large plenum design. Benefiting from the coolant outlet temperature controlled around 500 degrees C, the corrosion of T91 cladding was limited, resulting in a minimal oxide layer thickness that poses no significant threat to cladding structural integrity. However, at high burnup stages, the T91 cladding exhibited pronounced irradiation swelling specifically within the critical temperature range of 420 +/- 40 degrees C. To ensure cladding deformation remains within acceptable limits, T91 should avoid operation within this swelling-sensitive temperature range. Power ramp sensitivity analysis reveals that elevated fuel temperatures drive fuel restructuring-induced central void formation, significantly influencing the thermomechanical response. In specific, the central void formation could reduce fuel center temperatures and alleviate contact pressure during pellet-cladding mechanical interaction. These findings provide valuable insights crucial for the design and optimization of LBEFR fuels.

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.

This study focuses on the uncertainty and sensitivity analysis of containment aerosol removal under severe accident conditions using the severe accident integral code ASTEC, validated by experiments carried by CNPRI, and the coupled uncertainty and sensitivity tool SUNSET. A "separated" methodology is introduced, which distinguishes between thermal-hydraulic and aerosol boundary conditions to highlight the impact of containment aerosol mechanisms. Eleven uncertainty input parameters related are selected for uncertainty propagation and the Spearman correlation coefficient is utilized to describe sensitivity. The removal factor exhibits significant variation, with peaks due to temporary enhancement of diffusiophoresis deposition caused by corium relocation and subsequent steam generation. Uncertainty in the removal factor is substantial before aerosol release termination and narrows afterward. The gravitational settling and the diffusiophoresis deposition are the most important contributors with approximately 70 % and 20 % deposited aerosol mass, respectively. The initial mass medium diameter, the gravitational shape factor and the agglomeration shape factor are the most influential input parameters in aerosol removal, including in-containment deposition and environmental release, and they should be considered discreetly with conservatism in source term estimation and radioactive consequence evaluation in nuclear engineering.

Kenya's nuclear energy ambitions require rigorous safety and environmental assessments for proposed sites such as the Kilifi Nuclear Power Plant (NPP). This study models radiological consequences of a severe station blackout-induced LOCA using RASCAL (source term), HotSpot (atmospheric dispersion), and ERICA (ecological risk). Kilifi's coastal climate presents neutral stability (mean wind speed: 4.86 m/s) but complex dispersion behavior. The release was dominated by noble gases (75.6%) and iodine (24.2%). Peak Total Effective Dose (TED) reached 4.3 & times; 10(-3) Sv under Class C conditions, with mitigation reducing TED by similar to 99.9%. Terrain shielding reduced I-131 groundshine by over tenfold in urban areas. Raising release height from 50 m to 100 m reduced TED by 99%, with limited benefit above 200 m. Ecological risks were highest for Cs-137 (aquatic, RQ 66.9%) and Ru-106 (sediment, RQ 68.9%). HotSpot validation using Kincaid SF6 data confirmed its conservative reliability. Findings inform Kenya's nuclear siting, emphasizing iodine protection, terrain-aware planning, and long-term environmental monitoring.

The Reactor Vessel Cooling System (RVCS) plays a crucial role in passive residual heat removal for small leadcooled fast reactors. This study employs ACENA, a multi-phase, multi-physics coupling analysis code independently developed by Xi'an Jiaotong University, to assess the thermal performance of STAR-LM, a representative small-scale lead-cooled fast reactor, under RVCS operation during accident scenarios. The results confirm the effectiveness of the RVCS in significantly reducing coolant and vessel wall temperatures, ensuring reactor safety. However, a gradual decline in heat removal capacity is observed, primarily due to a decreasing air temperature difference. Sensitivity analysis reveals that increasing the chimney height enhances natural circulation and heat dissipation, whereas enlarging the hydraulic diameter of the RVCS loop structure notably weakens its cooling efficiency. These findings provide valuable insights into the safety assessment of small liquid-metal-cooled fast reactors and contribute to the optimization of RVCS design and operation strategies.

The fuel assembly is a core component of nuclear reactors, whose performance directly impacts the economics and safety of nuclear power systems, as well as key performance indicators of nuclear power installations. Within the harsh operating environment of a nuclear reactor-characterized by high temperature, high pressure, intense radiation, and long operational lifespan-the safe operation of nuclear fuel elements faces significant challenges. Therefore, the development of advanced nuclear fuel technology remains a critical strategic priority for leading nuclear power nations worldwide. In particular, out-of-pile verification and in-service performance evaluation techniques for nuclear fuel elements represent key research focuses and challenges in the field. To support the research and development needs of China's independent advanced nuclear fuel programs, several key experimental facilities have been established at Xi'an Jiaotong University. These include a flow-induced vibration test bench for 5 & times;5 fuel assemblies, a full-scale fuel rod crud deposition and corrosion test facility, a fuel assembly lower tie plate debris filter test rig, and a test platform for deformation behavior of sodium-cooled fast reactor fuel assemblies. Through these facilities, essential performance data have been obtained, including flow-induced vibration characteristics of China's proprietary fuel designs, crud deposition and corrosion behavior on cladding surfaces, debris filtration performance of lower tie plates, and core-level thermal deformation characteristics of fast reactor fuel assemblies. By establishing a series of out-of-pile experimental platforms, the simulation of key nuclear fuel phenomena has been achieved externally, replacing in-pile experiments and reducing the costs associated with experimental research for nuclear fuel performance evaluation and design improvements. Furthermore, a multidimensional, multi-physics coupled analysis platform for nuclear fuel elements, named BEEs and based on the finite element method, has been developed. This platform supports design analysis for a wide range of fuel types, including conventional rod-type fuel, accident-tolerant fuel (such as Cr-coated cladding and SiC cladding), annular fuel, plate fuel, helical fuel, spiral-rib fuel, TRISO dispersed fuel, and heat pipe reactor fuel. The implementation of refined geometric modeling for nuclear fuel elements and assemblies has significantly improved simulation accuracy, enabling precise characterization of key nuclear fuel phenomena. Furthermore, large-scale parallel computing has been leveraged to fully utilize computational resources, resulting in a substantial enhancement of computational efficiency. In addition, by establishing multiphysics coupling interfaces with open-source neutronics code OpenMC and in-house subchannel code SACOS, among others, the BEEs platform enables coupled neutronics-thermal-mechanical-fluid simulations. It provides technical support for the fuel design and development of advanced pressurized water reactors, gas-cooled microreactors, heat pipe reactors, and other reactor types. The development of nuclear fuel performance analysis software and the establishment of simulation platforms have propelled the construction of numerical reactors, enhanced nuclear fuel analysis efficiency, and played a vital role in fuel performance assessment and design optimization.

Thermal-hydraulic safety analysis of the nuclear reactor system features complex models, strong multi-parameter coupling, and stiff, highly nonlinear, ill-posed equations. Key technical issues are: first, how to improve the accuracy of key models and enhance the fidelity of system analysis codes while ensuring solution stability; second, how to optimize and update calculation results based on real-time data in code calculation; third, how to efficiently and accurately determine critical parameter weights in result evaluation technology; fourth, how to reasonably analyze the reliability of component models and perform optimization analysis. This review focuses on accuracy improvement and intelligent evaluation in reactor thermal-hydraulic safety analysis. It reviews intelligent optimization and evaluation technologies for nuclear power system analysis and discusses their application throughout the lifecycle of system analysis code development, optimization, evaluation, and application. To improve the prediction accuracy of basic models for nuclear power system analysis, an optimization method for key models based on artificial intelligence algorithms is developed. To address the low prediction accuracy and narrow applicability of constitutive equations, auxiliary equations, and other closure models, deep learning is used to construct AI models with high-dimensional nonlinear mapping capabilities. A combination of experimental data-driven approaches and physics-informed coupling strategies is adopted to improve prediction accuracy and applicability while ensuring computational robustness. Considering the accumulation of prediction errors over time, short-term corrective prediction for key reactor parameters is introduced to provide decision-making support. Data assimilation is employed to achieve real-time prediction optimization in reactor system analysis; correction methods for field and model parameters are developed based on measured data to update real-time prediction results and calibrate theoretical models, thereby improving the accuracy of dynamic model states and enhancing real-time prediction capability. For intelligent evaluation, a multidimensional framework for result evaluation and uncertainty quantification is constructed. Targeting the difficulty of evaluating real-time multi-source, multidimensional, non-uniform time-step data and the need to capture characteristic points and trends under special reactor transients, dynamic intelligent evaluation technologies integrating data dimensionality reduction, multi-parameter evaluation, and sensitivity analysis are developed. Accounting for modeling errors introduced by assumptions, simplifications, and approximations, as well as random data errors from measurement methods, surrogate-model acceleration and intelligent sampling are used to quantify the impact of input parameter uncertainty on calculation results, thereby identifying important input parameters and supporting uncertainty reduction and improvement of system analysis code models. Finally, based on prediction and evaluation results, reliability assessment of key system components using intelligent algorithms and deep learning is carried out to support multi-objective optimization design. Considering the randomness of physical processes and uncertainties of models and numerical methods, digital validation tests and fault tree analysis are employed to assess the reliability of nuclear power system equipment and reduce the risk of failure caused by accumulated uncertainties. In view of the multidimensional performance requirements and strong constraint-feedback characteristics of nuclear reactor systems, intelligent optimization techniques and deep learning algorithms for high-dimensional discrete data are integrated to realize multi-objective optimization of system analysis methods, thus meeting intelligent design needs for complex systems with multi-parameter performance constraints. Future work will establish best practice guidelines for intelligent optimization and evaluation technologies in nuclear reactor thermal hydraulic analysis and develop novel AI-based thermal hydraulic safety analysis methods, providing efficient and reliable tools for optimization design and safety review.

Small-scale closed Brayton-cycle systems require compact air-cooled heat exchangers that provide high heat-rejection capacity within strict mass and volume constraints while limiting auxiliary air-moving power. Additive manufacturing expands the topology space beyond conventional manufacturing, but internal overhangs remain constrained by process-specific support requirements. A tree-like self-supporting fin (TSF) is proposed by embedding a nominal laser powder-bed-fusion 45° branch-axis overhang rule into topology generation. The two-level branched fin is screened against this geometric rule, a literature-informed lower feature-size bound and open-flow-space requirements, establishing a self-support-oriented configuration for CFD assessment. Its thermal-hydraulic performance is compared with that of a conventional annular fin using a local-unit CFD model subjected to spatial mesh-sensitivity assessment and canonical correlation comparisons. Within the same envelope, the modeled TSF enlarges the heated area by about 1.8-fold and raises the LMTD-based heat-transfer coefficient by about 3.1-fold at a 2.6-fold static-pressure-drop penalty, giving a PEC-type index of about 2.28 at matched inlet velocity. At matched Pₛ = ΔpₛV̇, used as a local-unit static-pressure flow-power proxy, the interpolated heat-transfer rate is about 3.9 times that of the annular baseline at the reference operating point, and the modeled advantage persists over 2–12 m·s⁻¹. These results establish the air-side convective potential of the TSF under controlled constant-property, isothermal-wall conditions and provide a basis for system-coupled and experimental development of compact Brayton-cycle heat rejection.

Droplet impact on a liquid film is ubiquitous omnipresent and very fundamental in nature and industrial. For instance, in the spray cooling of the lower head of reactor pressure vessels, the method can enhance the safety margin of reactors. Extensive research has been carried out on the vertical impact of multiple droplets or single droplet on liquid films. However, the dynamical characteristics of multiple droplets impacting inclined liquid films remain insufficiently understood. Moreover, simulation approaches have predominantly concentrated on the Volume of Fluid (VOF) method. Therefore, this study attempts to conduct an in-depth numerical investigation of this phenomenon using the lattice Boltzmann method (LBM). A computational model was developed based on the Q3D27 and validated through benchmark cases involving single-droplet impacts on liquid films under both vertical and oblique conditions. The model accurately predicted key characteristics such as the outer diameter of the crown splash and the upstream crown radius. Based on the validated model, simulations of oblique impacts by dual droplets on a thin liquid film were conducted. The interfacial evolution was systematically analyzed, including the formation and development of crown splashes as well as the dynamics of intermediate thin-film jets. Furthermore, the Plateau-Rayleigh instability theory was employed to investigate the breakup mechanisms of liquid columns under varying impact angles and velocities. The fluid dynamic interactions between the two droplets under oblique impact conditions were also examined in detail, revealing complex flow behaviors relevant to multiphase flow dynamics.

The full-scale three-dimensional (3D) distribution characteristics of thermal-hydraulic parameters in a Steam Generator (SG) are crucial for the performance evaluation and safety analysis, which determine the economic efficiency and safety of nuclear power systems under long-term operation. Therefore, improving the prediction accuracy of the SG's 3D thermal-hydraulic field is one of the main development directions for SG analysis codes. A full-scale, tube-level computational model of the 55/19B steam generator (SG) was constructed using STEAM (Steam generator Tube-level thErmal-hydraulic Analysis platforM), a high-fidelity 3D code incorporating a two-fluid model developed by the Nuclear Thermal-hydraulic Laboratory at Xi'an Jiaotong University (XJTU-NuTHeL). Detailed thermal-hydraulic analysis was conducted for both the primary and secondary sides of the SG. Regarding the secondary side fluid domain, the simulation accurately reproduced the low void fraction distribution in the central bending tube region. Furthermore, areas susceptible to flow-induced vibration were pinpointed by analyzing crossflow energy. In the SG primary side flow domain, the characteristics of flow distribution in tube bundles were obtained, with a dimensionless standard deviation of 0.1 for the flow rates of the 4474 tubes. The influence of the channel head structure on flow distribution was also analyzed. Research on the high-fidelity tube-level 3D distribution characteristics of key thermal-hydraulic parameters on both sides of a full-scale SG can provide critical data support for SG flow-induced vibration analysis and design optimization.

The steam generator is very important for the safe operation of the whole nuclear power plant. During the long operation of the steam generator, the foreign objects carried by the secondary fluid will cause the vibration, wear, fatigue and even rupture of the heat transfer tube, which will affect the safety and reliability of the steam generator. In order to avoid serious damage to the steam generator by foreign matter, it is necessary to evaluate the impact of foreign objects on the steam generator tube and take appropriate measures according to the evaluation of results. In this study, CFD coupled with Discrete-Element Method was used to build a numerical simulation method for the calculation of foreign objects movement on the secondary side of steam generator to carry out numerical simulation of foreign objects movement trajectory, describe the dynamic characteristics of foreign objects, and obtain the law of foreign objects movement on the secondary side of steam generator.