Liquid-fuel thorium molten salt reactors (LF-TMSRs), as a candidate technology for Generation IV advanced nuclear reactors, offer significant advantages such as enhanced safety, non-proliferation, and reduced nuclear waste. However, fission gases generated during operation, such as xenon and krypton, reduce reactor efficiency and stability. To mitigate the accumulation of neutron poisons, techniques such as helium injection, mass transfer, and bubble separation are employed. Consequently, gas-liquid separation has become a critical technology in the operation of LF-TMSRs, with the swirl vane gas-liquid separator serving as a key component in the gas removal system. This study summarizes experimental approaches for evaluating separation performance and investigating separation mechanisms in swirl vane gas-liquid separators, providing guidance for future research.

Gas-liquid separation experiments in TMSRs can be divided into separation characteristic experiments and flow field characteristic experiments. This paper reviews recent studies in both categories, summarizes the features of experimental systems and techniques, and provides suggestions for future research. Separation characteristic experiments focus on evaluating parameters such as separation efficiency and critical back pressure to assess separator performance and determine optimal operating conditions. The experimental setup typically includes subsystems for bubble/liquid supply, flow and pressure regulation, parameter measurement, and liquid circulation. Flow field characteristic experiments utilize visualization systems to analyze two-phase dynamics, including air core morphology, bubble trajectory and morphology, and liquid phase velocity fields, using advanced technologies such as high-speed imaging and particle image velocimetry.

The different forms of the gas phase, such as the air core and bubbles, serve as visual indicators of separation performance. Ideally, a stable, rod-like gas core forms within the separator, and the bubbles concentrate along the central axis before discharge. However, factors such as back pressure, Reynolds number, and swirl number can cause deviations in air core morphology. Additionally, the bubble axial separation length, measured from initial injection to final coalescence into the air core, reflects the difficulty of separation. Shorter separation lengths usually correspond to higher separation efficiency, indicating satisfactory design or operating conditions. To further understand the microscopic separation mechanism, studies have examined swirl flow velocity distribution and bubble morphological evolution. Quasi-Rankine vortices dominate the flow structure, with swirl intensity initially increasing and then decreasing. As bubbles approach the central axis of the separator, their shapes flatten and eventually form asymmetric cap or cashew-like morphologies due to centrifugal and pressure gradient forces induced by the swirl flow.

Given the complexity of the flow dynamics within the separator, extensive experimental studies have evaluated separator performance and investigated underlying mechanisms. Future research should focus on extending experiments to hot-state conditions, collaboratively optimizing guide vane parameters, and developing a unified theoretical model to describe the complex bubble interactions, thereby advancing the understanding of gas-liquid separation mechanisms in LF-TMSRs.

The rapid development of big data and artificial intelligence (AI) technologies has greatly advanced research on magnetic confinement fusion (MCF), which is a promising approach to achieving sustainable fusion energy. Because of the complexity of plasma dynamics, including nonlinear and high-dimensional processes, conventional methods for simulation, monitoring, and control have faced significant limitations in accuracy and computational efficiency. By integrating AI and big data, researchers can now address many of these challenges, leading to significant improvements in the precision and speed of simulations as well as real-time analysis of experimental data. The application of these technologies has become essential for advancing fusion research, particularly in facilitating the control and optimization of plasma confinement, a key factor in achieving sustained nuclear fusion reactions.

The application of AI in MCF has led to numerous advances across different areas. In plasma simulation, AI-based methods, such as machine learning-driven surrogate models, have significantly reduced computational costs while maintaining or even enhancing accuracy. These models enable the rapid prediction of plasma behavior in response to varying conditions, which is crucial for optimizing experimental parameters and ensuring the stability of plasma confinement. The capability of AI to manage large-scale, high-dimensional data has proven particularly beneficial for multiscale simulations that involve complex interactions between physical processes. In experimental monitoring and control, AI, combined with big data analytics, has enabled real-time processing of sensor data from fusion devices. Through predictive modeling and adaptive control mechanisms, AI algorithms can detect potential anomalies and make autonomous adjustments to operational parameters, thereby improving the reliability and safety of fusion experiments. The dynamic nature of plasma requires precise and immediate responses to fluctuations, and the capability of AI to analyze past experimental data and predict future behavior has enabled more effective management of plasma instabilities, thereby enhancing the overall system robustness and contributing to the optimization of plasma performance. AI has also been instrumental in the design and optimization of fusion devices. By employing AI to model magnetic field configurations and predict material performance under extreme conditions, researchers have been able to improve the durability and efficiency of critical reactor components. These advancements include optimizing the design of superconducting magnets and plasma-facing materials, both of which are essential for the long-term operation of fusion reactors. AI-driven optimization has resulted in improved magnetic confinement configurations, ensuring better plasma stability and enhanced confinement performance, which are necessary for achieving continuous fusion reactions. Furthermore, AI facilitates interdisciplinary collaboration by integrating data from diverse fields, such as plasma physics, materials science, and computational modeling. The use of AI in cross-disciplinary research fosters innovation and accelerates progress in addressing key challenges in fusion research. Moreover, AI has contributed to the development of intelligent educational platforms and virtual experimentation environments, enabling researchers and students to gain hands-on experience through simulations and virtual experiments. These platforms are crucial for advancing knowledge and skills in plasma physics and fusion technology and help cultivate the next generation of fusion researchers.

The future of MCF research will be increasingly shaped by the integration of AI and big data technologies. The capability of AI to enhance simulation accuracy, optimize experimental design, and improve real-time control systems will play a central role in overcoming existing technical barriers in fusion research. Furthermore, AI-driven materials research will contribute to the discovery and design of new materials capable of withstanding the harsh conditions inside fusion reactors, thus ensuring longer operational lifespans and increased reactor efficiency. As AI technologies continue to evolve, they are expected to play a more significant role in all levels of fusion research, from experimental planning to real-time plasma control and material optimization. These advancements will not only accelerate progress toward realizing practical fusion energy but also contribute to the development of novel technologies that support the broader scientific community. In addition, AI-powered educational platforms will continue to provide researchers with advanced tools for learning and experimentation, helping them bridge the gap between theory and practical application. The continued development of AI and big data in this field holds great promise for the successful realization of MCF as a viable energy source for the future.

High-temperature gas-cooled reactors (HTGRs), a key component of fourth-generation nuclear reactors, are attracting considerable global interest. Graphite materials, serving as crucial structural materials and moderators in HTGRs, play a substantial role in reactor design, operation, and maintenance. A particular area of focus is the tribological properties of graphite.

In response to the limitations of existing methods used for measuring the friction coefficient of graphite at high temperatures, pressures, and load conditions, a new method has been proposed in this article. This method relies on a self-designed graphite-based device, which simulates the environmental parameters within the core of an HTGR, thereby allowing the measurement of the coefficient of friction of graphite materials. The graphite material used in this study is BG80, which fulfills the requirements of HTGRs well. Gas atmospheres of the measurement tests comprised helium and nitrogen. The gases were used separately to achieve different atmospheric conditions. The temperature range of the device can be changed from 25 ℃ to 1 300 ℃, which includes normal and ultrahigh temperatures as well as those reached during accidents in HTGR power plants. The load range of the device is 0~30 kg, which is close to the load experienced by graphite materials in the reactors. The device can be internally heated by a self-designed graphite heater powered by direct current. Further, the temperature of the device can be well controlled using an auxiliary system. The device shell is made of stainless steel, with a cavity constructed from insulating bricks and filled with insulation cotton to maintain the temperature. Graphite rods are used for heating, and force sensors are used to collect data on pushing and pulling forces, which are used to accurately calculate the friction force and coefficient of friction. Data processing involves measuring frictional forces via a sensing device when pushing forces are applied at a fixed rate under a given temperature and atmospheric condition as well as under a given load in the normal direction. To mitigate measurement errors, differences between pushing, pulling, and normal loading forces are used for data analysis. Then, linear regression is employed to determine the coefficient of friction from the measured forces.

The results demonstrate that the measurement method can stably and accurately obtain data on the coefficient of friction of graphite materials. In addition, generally, when the displacement is greater than 5 mm, the coefficient of friction begins to stabilize during subsequent displacement processes. Concurrently, when the temperature is >500 ℃, the coefficient of friction in helium significantly decreases compared to that below 500 ℃. The results of this study provide support for the numerical simulations and design improvements of HTGRs.

Continuous enhancement of energy efficiency is an essential element of China's green development plan to meet the peak carbon dioxide emission target by 2030 and carbon neutrality objective by 2060. Boiling heat transfer, being one of the most effective methods for phase-change heat transfer, is crucial for heat-energy transfer and conversion in various industries. Therefore, improved boiling performance can increase the efficiency, safety, and cost-effectiveness of energy systems. Graphene, a novel material discovered at the turn of the 21st century, has exceptional properties in numerous fields and can be used as nanoparticles for enhancing the heat transfer of base fluids. This research study aims to enhance boiling heat transfer by examining the effects of the heating surface and working fluids, specifically with graphene nanofluids.

Reduced graphene oxide (RGO) nanofluid, which is a product of graphene preparation via the redox method, was utilized as the working fluid. The experimental investigation aimed to examine the heat-transfer characteristics of RGO-nanofluid saturated pool boiling at atmospheric pressure. The experimental data were collected and analyzed using a high-speed camera to record the morphology of vapor bubbles during boiling. The research study also used a pool-boiling experiment with a pure copper heating surface and distilled water as the working fluid to provide benchmark data to compare with the RGO nanofluid experiment.

The results indicated that the RGO nanofluids had a significant impact on the critical heat flux (CHF) of pool boiling, which reached 1 684.22 kW/m², increased by 49.2% compared to distilled water. However, the nanofluids did not significantly affect the heat transfer coefficient (HTC) of pool boiling, which reached 73.87 kW/(m²·K), only increased by 2.3% compared to distilled water. At a constant heat-flow density, the effect of RGO nanofluids on the superheated wall was insignificant. Further analysis of the experimental data revealed that the RGO deposition layer formed by the RGO nanofluids on the heated surface during boiling was the core factor contributing to the increase in CHF. The deposition layer changed the wettability and vaporization core number of the surface, reduced the detachment diameter of vapor bubbles on the heated surface, and increased the detachment frequency, which delayed the appearance of CHF. This was supported by the measurement of the contact angle of the heated surface, surface observation of the heated surface after boiling, and analysis of the vapor bubble visualization images.

In conclusion, this study demonstrated that the use of RGO nanofluids can significantly improve the critical heat flux of pool boiling, which can contribute to the efficiency, safety, and cost-effectiveness of energy systems. The results also provide insights into the mechanism of heat transfer enhancement through the use of RGO nanofluids, specifically through the formation of a deposition layer on the heated surface during boiling. These findings can have practical implications in various industrial applications, including nuclear reactors, electronic cooling systems, and heat exchangers. However, further research is necessary to optimize the use of graphene nanofluids in various industrial applications and to assess their long-term effects on energy systems.

Condensing heat exchange is a crucial process in the primary circuit of small modular reactors and passive safety systems that rely on natural circulation as the driving force. With the higher requirements for heat exchange efficiency and reactor safety, in-depth research and an understanding of the condensation heat exchange process are needed. Therefore, numerical simulations of the condensing heat exchange process have attracted increasingly more interest. However, due to the complex phase change, the condensing heat exchange process is difficult to model using analytical equations. Traditional numerical simulation methods use the empirical equations summarized in experiments, and their universalities are controversial. In contrast, the lattice Boltzmann method is a mesoscopic-level numerical simulation method that tracks particle clusters and uses probability density functions to describe their distribution, resulting in a simple and clear structure that appropriately ignores the details of molecular motion. Moreover, it allows direct iterative solving of the probability density distribution function without relying on empirical equations. In previous studies, the feasibility of using the lattice Boltzmann pseudopotential model in condensation process simulation was verified. Subsequently, numerous researchers have used this model to analyze the condensation mechanism.

This study is based on the lattice Boltzmann method and uses a dual distribution function to simulate the condensation process of stationary saturated vapor within a vertical channel. To analyze the fluid flow characteristics, a pseudopotential model is used to simulate the density field variations during the vapor condensation. Additionally, a temperature distribution function is employed to simulate the temperature field changes during the vapor condensation, allowing for an examination of heat transfer efficiency. Throughout the simulation, we analyze the effects of channel width and the hydrophilicity and hydrophobicity of wall conditions on the condensate flow and heat transfer rate.

The results showed that: 1) When saturated vapor encountered a hydrophilic wall, it first condensed to form a thin liquid film covering the entire wall surface and then formed a steady liquid film from the top of the vertical channel, gradually expanding downward. Due to the pressure difference caused by the vapor condensation, the saturated vapor flowed down into the channel from the inlet at the top of the channel. 2) Under hydrophilic wall conditions, decreasing the channel width from 500 to 150 decreased the steady-state average mass flow rate at the inlet by approximately 20% and decreased the steady-state average heat flux density on the wall by approximately 6.5%. 3) The simulation results under different hydrophobic and hydrophilic characteristics were consistent with the theoretical analysis, indicating that the stronger the wall hydrophobicity was, the later the starting time of droplet nucleation and the lower the starting point of the vertical liquid film. On the ordinary hydrophobic wall surface, the droplet condensation was difficult to sustain, and after this surface was covered by a liquid film, the heat transfer rate was slower compared to the hydrophilic wall surface. Before the liquid film slipped out of the computational domain, the maximum average wall heat flux at an angle of 127° was approximately 75.8% of that at an angle of 51°.

The lattice Boltzmann pseudopotential model can simulate the condensation process of stationary saturated vapor within a vertical channel. During the simulation process, the effects of channel width and the hydrophilicity and hydrophobicity of wall conditions on condensate flow and heat flux density are important. In general, wider channel widths lead to higher wall heat flux density, and a higher inlet mass flow rate of steam is achieved at a steady state for saturated vapor initially in a stationary state within the channel. However, ordinary hydrophobic wall surfaces cannot sustain droplet condensation and do not demonstrate enhanced heat transfer. These findings have certain reference values for designing and optimizing heat transfer systems involving condensation in vertical channels.