Coated particle fuel concepts date back some 60 years, and have evolved significantly from the relatively primitive pyrocarbon-coated kernels envisioned by the first pioneers. Improvements in ...particle design, coating layer properties, and kernel composition have produced the modern tristructural isotropic (TRISO) particle, capable of low statistical coating failure fractions and good fission product retention under extremely severe conditions, including temperatures of 1600 °C for hundreds of hours. The fuel constitutes one of the key enabling technologies for high-temperature gas-cooled reactors, allowing coolant outlet temperatures approaching 1000 °C and contributing to enhanced reactor safety due to the hardiness of the particles. TRISO fuel development has taken place in a number of countries worldwide, and several fuel qualification programs are currently in progress. In this paper, we discuss the unique history of particle fuel development and some key technology advances, concluding with some of the latest progress in UO2 and UCO TRISO fuel qualification.
•Coated-particle fuel has matured over six decades of development.•Early evolution involved a wide variety of a different coating configurations.•Modern TRISO particle design can maintain coating integrity under extreme conditions.•Fission products are largely retained at their source in the fuel kernel.•Several major programs on TRISO fuel qualification are currently in progress.
•The study proposed an efficient simplified elastic-plastic analysis procedure using engineering formulae for performing strain-based fatigue design of nuclear safety class 1 piping system during ...severe seismic loads.•The proposed procedure was applied to a pressurizer surge line system.•The total strain amplitudes, fatigue assessment results, and total calculation time were compared between the proposed analysis procedure and the detailed dynamic finite element elastic-plastic analysis.•The comparison confirmed that the proposed simplified elastic-plastic analysis procedure can efficiently derive reasonable and conservative results.
This paper proposes an efficient simplified elastic–plastic analysis procedure using engineering formulas to perform strain-based fatigue design considering the plasticity that occurs in nuclear safety class 1 piping systems during severe seismic loads, and then presents the application results of the procedure to a pressurizer surge line system. The total strain amplitudes, fatigue assessment results, and total calculation time were compared between the proposed procedure and the detailed dynamic finite element elastic-plastic analysis. The comparison confirmed that the proposed analysis procedure can efficiently derive reasonable and conservative results for nuclear safety class 1 piping systems under severe seismic loads.
While lithium metal represents the ultimate high-energy-density battery anode material, its use is limited by dendrite formation and associated safety risks, motivating studies of the ...solid-electrolyte interphase layer that forms on the lithium, which is key in controlling lithium metal deposition. Dynamic nuclear polarisation enhanced NMR can provide important structural information; however, typical exogenous dynamic nuclear polarisation experiments, in which organic radicals are added to the sample, require cryogenic sample cooling and are not selective for the interface between the metal and the solid-electrolyte interphase. Here we instead exploit the conduction electrons of lithium metal to achieve an order of magnitude hyperpolarisation at room temperature. We enhance the
Li,
H and
F NMR spectra of solid-electrolyte interphase species selectively, revealing their chemical nature and spatial distribution. These experiments pave the way for more ambitious room temperature in situ dynamic nuclear polarisation studies of batteries and the selective enhancement of metal-solid interfaces in a wider range of systems.
To efficiently capture the energy of the nuclear bond, advanced nuclear reactor concepts seek solid fuels that must withstand unprecedented temperature and radiation extremes. In these advanced ...fuels, thermal energy transport under irradiation is directly related to reactor performance as well as reactor safety. The science of thermal transport in nuclear fuel is a grand challenge as a result of both computational and experimental complexities. Here we provide a comprehensive review of thermal transport research on two actinide oxides: one currently in use in commercial nuclear reactors, uranium dioxide (UO2), and one advanced fuel candidate material, thorium dioxide (ThO2). In both materials, heat is carried by lattice waves or phonons. Crystalline defects caused by fission events effectively scatter phonons and lead to a degradation in fuel performance over time. Bolstered by new computational and experimental tools, researchers are now developing the foundational work necessary to accurately model and ultimately control thermal transport in advanced nuclear fuels. We begin by reviewing research aimed at understanding thermal transport in perfect single crystals. The absence of defects enables studies that focus on the fundamental aspects of phonon transport. Next, we review research that targets defect generation and evolution. Here the focus is on ion irradiation studies used as surrogates for damage caused by fission products. We end this review with a discussion of modeling and experimental efforts directed at predicting and validating mesoscale thermal transport in the presence of irradiation defects. While efforts in these research areas have been robust, challenging work remains in developing holistic tools to capture and predict thermal energy transport across widely varying environmental conditions.
•This paper examines the use of machine learning models for fault diagnostics, specifically, the identification of transient events in a nuclear power plant to reduce human errors.•The data was ...collected from the Generic Pressurized Water Reactor (GPWR) simulator and processed using MATLAB.•A total of 9 different transient events were simulated with 12 different initial conditions to create a dataset with 72,000 data points.•The results of this study exhibited favorable comparisons with other machine-learning endeavors in the field of reactor transient detection and diagnostics.
Enhancing safety and dependability within nuclear power facilities holds paramount importance in safeguarding both individuals and the environment. The adoption of machine learning for diagnosing faults in these plants is steadily gaining interest, driven by its capacity to detect faults, alleviate human errors in high-pressure scenarios, and ensure the secure and consistent operation of these facilities swiftly and accurately.
This paper examines the use of machine learning models for fault diagnostics, specifically, the identification of transient events in a nuclear power plant to reduce human errors. The data was collected from WSC’s Generic Pressurized Water Reactor (GPWR) simulator and processed using MATLAB. The simulator encompasses models for both the primary and secondary systems of the nuclear power plant (NPP). Additionally, it incorporates models for the control systems and instrumentation responsible for monitoring and regulating the reactor, serving as integral components for data extraction and transient modeling. A total of 9 different transient events were simulated with 12 different initial conditions to create a dataset with 72,000 observations. Nine types of classification models (33 total preset models) were trained and validated using the classification learners application. Among them, the Neural Network Classifiers (NNC) displayed the highest average accuracy of 90%. The Fine Tree, Ensemble Bagged Trees, and Medium Neural Network models were the best-performing individual models with validation accuracies above 90% and a maximum training time of 8 min. These models were further analyzed using accuracy, confusion matrix, precision, recall, and F1 score. To optimize these models, techniques such as different validation schemes and feature selection were utilized to further reduce their training time and improve their prediction accuracy. The optimized models boasted comparable accuracies with a maximum training time of under 1.5 min. The results of this study exhibited favorable comparisons with other machine-learning endeavors in the field of reactor transient detection and diagnostics. Notably, the study maintained low execution and computation times while preserving high levels of accuracy. This study offers insightful information on how AI and machine learning can be used to improve nuclear power plant diagnostics, enhance safety, and provide support to the operator.
An analysis of the amount of hydrogen taking part in the explosions that happened during the Fukushima-Daiichi (Unit 1) nuclear power plant accident is presented herein. Through a series of analytic ...approximations and numerical calculations of increasing complexity, it has been possible to estimate that 130 kg of H2 was involved in the explosion. Also, the strength of the resulting explosion was examined determining that even with a significantly smaller amount of hydrogen taking part, a devastating explosion would have occurred regardless.
The phase diagram of four-dimensional Einstein–Hilbert gravity is studied using Wilsonʼs renormalization group. Smooth trajectories connecting the ultraviolet fixed point at short distances with ...attractive infrared fixed points at long distances are derived from the non-perturbative graviton propagator. Implications for the asymptotic safety conjecture and further results are discussed.
Abstract
The nuclear main pump has a complex structure and contains a large number of components. With the continuous flow of the primary circuit coolant, there is a risk of loosening and falling off ...of the components in the reactor. The existence of loose parts in the reactor makes the reactor operation a safety hazard. Accurate mass estimation of the loose parts is conductive to quickly judge the degree of the failure of loose parts, provide effective suggestions for the subsequent maintenance of the reactor and ensure the safety of reactor operation. The existing mass estimation methods have problems of large errors and poor consistency. This article proposes a method for estimating the mass of loose parts by establishing a functional relationship model based on signal characteristic value. The characteristic value of the signal is constructed by extracting the Root Mean Square value and the main frequency of the shock signal, and then the functional relationship between the quality of the loose part and the characteristic value of the signal is established, which is used to estimate the mass of the loose part. The experimental results show that the method proposed in this paper has the characteristics of small estimation error and good consistency, which can meet the needs of practical engineering applications.
The article is devoted to the analysis of legal regulation of the sphere of nuclear safety and security of Ukraine on the way to European integration. The authors drew attention to the importance of ...Ukraine achieving the necessary level of and nuclear sefaty and security adopted in the EU member states. The emphasis was placed on the fact that the prospects for fulfilling national obligations in the field of nuclear safety in accordance with European standards directly depend on solving the problems of ensuring the functioning of nuclear facilities, the physical protection of nuclear materials and installations as well as radioactive waste management. The main directions of ensuring the nuclear safety and secutiry in the world within the international law are considered. The role and activities of the International Atomic Energy Agency (IAEA) in setting up a regulatory framework for nuclear safety and security are analyzed. The international legal framework for nuclear safety and security was discused.The legislative basis for nuclear safety and secutiry in the EU IS characterized. The issue of legal norms unification in the field of nuclear safety regulation of EU member states was considered. The principles of legal regulation of nuclear a safety and security in Ukraine are characterized.
Key words: nuclear safety, nuclear security, public administration of nuclear safety and security, legal regulation of nuclear safety and security, European integration, sustainable development in the field of ensuring nuclear safety and security.
UDC 35:574:339.9:349.6 JEL Classification: K 23, K 32, K 33, Q 5
Edge-localized mode (ELM) suppression by resonant magnetic perturbations (RMPs) generally occurs over very narrow ranges of the plasma current (or magnetic safety factor q95 ) in the DIII-D tokamak. ...However, wide q 95 ranges of ELM suppression are needed for the safety and operational flexibility of ITER and future reactors. In DIII-D ITER similar shape plasmas with n = 3 RMPs, the range of q95 for ELM suppression is found to increase with decreasing electron density. Nonlinear two-fluid MHD simulations reproduce the observed q95 windows of ELM suppression and the dependence on plasma density, based on the conditions for resonant field penetration at the top of the pedestal. When the RMP amplitude is close to the threshold for resonant field penetration, only narrow isolated magnetic islands form near the top of the pedestal, leading to narrow q95 windows of ELM suppression. However, as the threshold for field penetration decreases with decreasing density, resonant field penetration can take place over a wider range of q95. For sufficiently low density (penetration threshold) multiple magnetic islands form near the top of the pedestal giving rise to continuous q95 windows of ELM suppression. The model predicts that wide q95 windows of ELM suppression can be achieved at substantially higher pedestal pressure in DIII-D by shifting to higher toroidal mode number ( n = 4 ) RMPs.
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