Operation Of Nuclear Power Plants Research Articles

Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants

The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.

Probabilistic typhoon hazard and sensitivity analysis for nuclear power plant sites in Korea using logic tree

There has been an increase in typhoon intensity, and high-intensity typhoons can affect the safety of nuclear power plants (NPPs). Typhoons can damage the structures, systems, and components (SSCs) of NPPs, causing core damage and the release of radioactive materials into the environment. Therefore, design criteria for the effects of natural phenomena are demanded to ensure the safety-critical SSC functions of NPPs. Also, there is a need for safety assessments of typhoons in operational NPPs, which require typhoon hazard analysis. In this study, we derived typhoon hazards for NPPs in Korea using Logic Tree and Monte Carlo simulations. We performed sensitivity analysis on the typhoon hazards based on the distances (up to 100 km) from the NPP sites. Among other NPP sites, the typhoon hazard of the Kori site showed the highest wind speed, and the Hanul site showed the lowest wind speed. The uncertainty of the wind speed increased as the recurrence interval of the typhoon hazard increased. We compared the typhoon hazard in the areas of interest to that within 100 km. The range of the distance deviation for the Hanbit site's typhoon hazard was within μ±σ up to 75 km, while for the other sites, it was within μ±σ up to 100 km.

Radial reflector macroscopic cross sections preparation impact on the low neutron leakage core Beavrs

Full core nodal calculations are essential for safe nuclear power plant operation. The accuracy and compliance with the calculation and the experiment are based on the proper macroscopic data preparation model. While fuel assembly data preparation is usually based on the simple infinite lattice model simulation, the reflector data preparation is not standardised and the influence of the reflector macroscopic data on full core simulation is not quantified. The paper evaluates the large core with low neutron leakage behaviour with the different reflector cross section preparation models with respect to criticality and power distribution prediction. However, in opposition to the first insight, the different reflector model cannot increase the compliance with the power prediction near the fuel assembly reflector boundary. However, the usage of a special rehomogenization model can be helpful for pin power reconstruction compliance near the edge of the core.

Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest

Data-driven fault diagnosis techniques are significant for the stable operation of nuclear power plants (NPPs). However, in practical applications, the fault diagnosis of NPPs usually faces imbalance data problems with small fault samples and much redundant data which results in low model training efficiency and poor generalization performance. Thus, this paper proposes a convolutional variational autoencoding gradient-penalty Wasserstein generative adversarial network with random forest (CVGR) to reduce the impact of imbalanced samples on fault diagnosis. Firstly, a feature selection method based on the random forest is used to identify the most relevant measurements and reduce the impact of redundant data on fault diagnosis. Then, variational autoencoding is introduced into gradient-penalty Wasserstein generative adversarial to effectively extract original sample features and generate high-quality samples with high rationality and diversity. In addition, the convolutional neural network is used to extract the features of mixed samples to realize intelligent fault diagnosis. Finally, several experiments based on the Fuqing Unit 2 full-scope simulator under different operating conditions are used to validate the performance of the CVGR in data enhancement and intelligent fault diagnosis. The results show that the proposed method can effectively mitigate the imbalance data problem, which gives insights into intelligent fault diagnosis of NPPs.

Optimal design of circulating water system in pressurized water reactor nuclear power plant

The optimization of the circulating water system of pressurized water reactor nuclear power plants can effectively improve the profit without affecting the primary circuit operation, which is of great significance in enhancing the market competitiveness of nuclear power plants. The circulating water system of the nuclear power plant is modeled, and the circulating water system optimization scheme is formed after the nuclear power unit is reformed from three aspects: condenser heat transfer area, condenser connection mode, and circulating water pump operation mode. The circulating water system optimization calculation is carried out for each scheme by using the minimum annual cost method. Based on the sensitivity analysis of the factors that affect the profits of nuclear power plants, the best circulating water system optimization scheme is finally given for new nuclear power plants and nuclear power plants in operation, which provides a reference for the construction of related nuclear power projects.

Low-Power Wireless Sensor Module for Machine Learning-Based Continuous Monitoring of Nuclear Power Plants.

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

MODELING THE DISTRIBUTION OF EMERGENCY RELEASE PRODUCTS AT A NUCLEAR POWER PLANT UNIT

Despite the fact that much attention is paid to the safe operation of nuclear power plants, there is a possibility of an accident with the release of radionuclides. This is especially true in Ukraine, where there is a threat of the damage to nuclear reactors as a result of military operations. It is impossible to research the distribution of products emergency releases radioactive substances in laboratory conditions. Therefore, the only tool for the development predicting of an accident is the modeling the spread of a radionuclides cloud. The purpose of the research is a modeling the distribution of emergency release products in a nuclear power plant unit, suitable for the operative assessment of a development an accident. Results of the research: The mathematical model of the distribution emission products of a nuclear power plant has been developed, which takes into account the value of the initial activity of emission products, the rate of the settling radioactive particles, the wind speed components, the intensity changes radionuclide emission over time. The technique for solving the boundary value problem of modeling in conditions of a complex shape of the computational domain, taking into account the presence of obstacles to the spread of emission products has been developed. The use of the velocity potential equation in evolutionary form allows us to speed up the calculation process. The chosen splitting scheme of an alternating-triangular method allows to find the speed potential according to the explicit form at each splitting step. This allowed software implementation of the CFD model. The visualized models of the emission cloud distribution allow to determine the radiation situation in any place of the emission product distribution zone. The developed model makes it possible to quickly predict the development of an accident in space and time, which makes it possible to take measures to protect people from exposure in the shortest possible time. Conclusions: The obtained emission cloud propagation models and their visualization make it possible to determine the state of environmental pollution under various initial conditions during the development of the accident.

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

A Study on One-Time Inspection for Aging Management of Operating Nuclear Power Plants

One-time inspection is used to confirm the effectiveness of nuclear power plant aging management programs such as water chemistry, fuel oil chemistry, and selective leaching program that is designed to prevent or minimize aging degradation during the plant operation. This paper represents one-time inspection experience of selected components and susceptible locations within the scope of water chemistry and fuel oil chemistry aging management programs. Prior to performing one-time inspection, sample sizes of components to be inspected were determined based on combination of materials, environment and aging effect. Examination techniques and relevant operating experience were analysed to obtain technical basis for further implementation of one-time inspection aging management programs. Existing correlation curves between sample size and total population number were reviewed to prepare further inspections for entire operating plants and ensure safety level of components subject to one-time inspection program.

How Cs-137 dispersions due to atmospheric deposition to East Vietnam Sea: a hypothetical level 7 incident from the Fangchenggang Nuclear Power Plant (China)

Only about 50 km from the Quang Ninh coastal area in Vietnam, the Fangchenggang Nuclear Power Plant (China) operation causes excellent concerns for the environment, especially the marine environment, if an incident occurs. Based on topographic and hydrometeorological data and assumptions about radiation release conditions when an incident occurs, the Delft3D-Part modeling system was set up to assess/forecast the effect of Cs-137 radioactive activity on the source of air deposition in the water environment of Vietnam. The results showed that the sources of the drop from the air significantly affected and caused a faster impact than direct radioactive sources after the incident. Under the influence of marine circulation, wind radioactive material after falling into the sea will quickly disperse and may affect the entire East Vietnam Sea after 3–6 months. The area with high radioactivity is concentrated mainly in the Tonkin Gulf and along the coast of Vietnam. Especially in the Gulf of Tonkin, the radioactivity can reach 300–1,200 Bq/m3 after the 5-day incident. The amount of radiation then gradually decreases to almost less than 100 Bq/m3 after one year and below 20 Bq/m3 after two years. According to depth, the amount of radioactive Cs-137 tends to shift between layers from the surface to the bottom: higher at first in the upper layers, then gradually decreasing and increasing in the lower layers. Radioactivity significantly reduced after two years of the incident but still exceeded QCVN 10: 2008/BTNMT.

Convolutional preprocessing Transformer-based fault diagnosis for rectifier-filter circuits in nuclear power plants

Rectifier-filter circuit, as a vital electronic component in nuclear power plants (NPPs), serves the role of converting alternating current into smooth direct current. Affected by intense radiation environmental factors and variable operating profiles, the rectifier-filter circuit is subject to degradation and soft failures, namely, a derated circuit performance but not yet to failure. It is urgent to diagnose the soft failures of the rectifier-filter circuit, so as to ensure the safe operations of NPPs. In this article, an ensemble empirical mode decomposition (EEMD) algorithm is first employed to decompose the three-phase current signals and electrical signals of electronic components. The decomposed signals are fed into a convolutional preprocessing Transformer (CPT) to deeply extract the fault features of the rectifier-filter circuit. Specifically, the multi-directional, multi-scale, and extremely sensitive long-range time-dependent features in the EEMD feature data are extracted by the multi-head attention mechanism of the Transformer. A deep Softmax classifier is, then, devised to reduce the feature dimensionality and identify the soft fault modes of the rectifier-filter circuit. The fault simulation of the rectifier-filter circuit is conducted, and a real case experiment and multiple comparative studies are conducted to validate the effectiveness and diagnosis accuracy of the CPT-based method.

Legal regulation of nuclear and radiation safety during operation of nuclear power plants

The article examines the problems of legal regulation of nuclear and radiation safety that arise during the operation of nuclear power plants. On the basis of the legal definition of such legal categories as radiation and nuclear safety, radiation protection, which must be guaranteed by the state to human life and health, the main attention is focused on the analysis of the legal regime of lands as the basis of human life activities that have undergone radioactive contamination, which are legally classified as man-made polluted. The genesis of the legislation that secures the definition of these lands is analyzed, it is emphasized that these lands are the result of human economic activity, that is, it is about the consequences of anthropogenic activity, when highlighting the peculiarities of the legal regime of man-made contaminated lands, their division into radioactively dangerous and radioactively contaminated is taken as a basis. The mentioned lands, being in state ownership, cannot be transferred to private ownership, are included in the exclusion zone and the zone of mandatory (unconditional) resettlement of the territory that has undergone radioactive contamination as a result of the Chernobyl disaster. Residence and permanent stay of people in the territory of these zones is prohibited by law. Land plots located in the zone of guaranteed voluntary resettlement are also radioactively contaminated and are used in accordance with the procedure determined by the Cabinet of Ministers of Ukraine. The amendments to the current legislation are analyzed separately, which relate to the inclusion of radioactively contaminated lands in the ecological network for the purpose of restoration and are related to the creation of the Chernobyl radiation- ecological biosphere reserve. It is emphasized that part of the territory of the reserve, created at the expense of the zone of exclusion and the zone of unconditional (mandatory) resettlement, being on the border with the Republic of Belarus, causes additional restrictions of its legal regime. In this case, we are talking about changing the purpose of a part of the relevant lands, which have been transferred to defense lands. The conclusions emphasize that lands that have undergone radioactive contamination need priority restoration, which today is carried out naturally. The legal mechanism for restoring the state of such lands is imperfect and needs significant changes. It is time to develop and approve the Strategy for returning radioactively contaminated land to economic use.

R&D in advanced technology fuels (ATFs) in Spain

Accident Tolerant Fuels, also known as Advanced Technology Fuels (ATFs), have the potential to improve the safety and economic performance of commercial nuclear reactors as well as to provide additional operational flexibility for responding to variations in system energy demand.The design, fabrication and in-pile behaviour of currently operating and innovative nuclear fuels to enable a reliable and safe operation of nuclear power plants is a priority for the nuclear industry. The Spanish nuclear professionals (public and private companies, academia, and research centres) have achieved significant advances in this area. Both irradiation programs in commercial reactors, along with experimental and modelling activities, were focused into comprehending and modelling different ATF concepts to understand and predict the behaviour under normal and off-normal conditions.The International Atomic Energy Agency (IAEA) sub-program “Nuclear Power Reactor Fuel Engineering” is strongly involved in the development and validation of computer codes for ATF fuel design and the valuable Spanish contribution to this sub-program is described in this article.The future Spanish R&D in this field is expected to be focused on further improvements on ATF product development and performance knowledge.

Simulation of human risk scenarios for nuclear power plant operation based on finite state machine

This paper addresses risk scenarios caused by Human-Machine Interaction (HMI) during the Nuclear Power Plant (NPP) operation. A simulation method is proposed in this research based on a Finite State Machine (FSM) for analyzing human-related risk scenarios in NPP operations. The risk evolution mechanism between the physical system and operator is modeled by FSM to clarify how risk accumulation leads to accidents. It has the advantage of managing and simplifying the modeling process of complex relationships in NPP with the integration of human reliability analysis. The study focuses on the dynamic interaction between the physical system in the primary loop and the operators during the startup of NPP and evaluates human-related safety risk through simulation of the behavior of the operators handling the control rods. This research aims to present an FSM-based methodology for analyzing HMI-induced risks in NPP operations, which can be utilized to illustrate the risk evolution, especially human-related risk.

Couple-stress nanofluid flow comprising gyrotactic microbes subject to convective boundary conditions: Numerical solution

Couple-stress nanofluids have multiple potential applications in numerous industrial and engineering sectors, such as energy production, medical diagnostics, thermal control systems, and the aerospace industry. Couple-stress nanofluids have the ability to improve the heat exchange properties and elevate the performance of nuclear power plants, solar panels, and other renewable energy sources. Therefore, in the current analysis, a non-homogeneous nanofluid model is considered to examine the non-Newtonian Casson nanofluid flow across a prolonging sheet. The flow has been studied under the significance of generalized Fourier’s and Fick’s laws, convective boundary conditions, and the heat source/sink. The modeled equations are simplified into a dimensionless lowest-order system of ordinary differential equations by using similarity transformation. The numerical outcomes are achieved by using the “ND-Solve” approach. It has been noticed that the energy field decreases because of the Prandtl number’s impacts, whereas it increases with the increase in the heat radiation parameter. The couple-stress nanoliquid’s velocity decreases vs increasing values of the magnetic field and mixed convection parameter. The influence of thermal relaxation and couple-stress parameters falls off the energy field. Furthermore, the intensifying effect of Rayleigh number and buoyancy ratio increases the fluid temperature.

Multi-scale boiling heat transfer investigation on micro-thin aluminum heaters

Nucleate boiling is a highly efficient heat transfer mode distinguished by the liquid-vapor phase change, which occurs through the formation, growth, and detachment of vapor bubbles from a heated surface. Its crucial role in various industrial applications, such as nuclear power plant operation and effective heat management in small electronic devices, has driven significant research efforts. However, despite extensive research dedicated to boiling investigations, there are still substantial knowledge gaps that hinder our ability to accurately predict heat removal rates. These knowledge gaps arise from the complex nature of small-scale boiling phenomena, which are further complicated by their strong dependence on operating conditions and the interactions between walls and fluids. In an effort to address some of these gaps, we conducted multi-scale investigations during pool boiling of de-ionized water on micro-thin aluminum heaters. We captured bubble dynamics through multiple synchronized diagnostic sources, including high-speed backlit imaging to track bubble growth, synchronized high-speed infrared thermometry to capture the corresponding thermal footprint on the boiling surface, and in-house developed fast-response micro-thermocouples to measure temperature at multiple locations within the fluid. Our study reveals peculiar aspects of heat transfer mechanisms occurring at single bubble level (low heat fluxes) and in fully developed nucleate boiling regimes (high heat fluxes).

원전 기동운전구간의 고장 감지 방법에 관한 연구

원전 기동운전구간의 고장 감지 방법에 관한 연구

ANN-based model integrated in thermal-hydraulics codes: A case study of two-phase wall friction model

Accurate prediction of two-phase parameters is essential for the development, operation and safety of nuclear power plants. In this paper, the ANN-based model has been developed, implemented with PDE (Partial Differential Equation) solver in case study of two-phase frictional pressure drop prediction.

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Efficient steam condensation is crucial for safe nuclear power plant (NPP) operations, preventing pressure buildup, overheating, and the release of radioactive materials. However, the presence of noncondensable gases (NCGs), such as air, nitrogen, hydrogen, or helium, can hinder the condensation process by creating a thermal resistance layer that impedes steam diffusion and condensation on the system’s surface. Maximizing the efficiency of steam condensation requires a thorough grasp of the fundamental processes, theories, advancements, and technical hurdles. Therefore, this work thoroughly addresses these needs, with a particular emphasis on addressing the challenges posed by NCGs by dividing the work into four thematic areas. The first theme relates to a comprehensive examination of pure steam condensation phenomena, which includes an exploration of familiar condensation scenarios and various film condensation types. The second theme examines condensation in the presence of NCGs, their mixture properties, and related theories and modelling of heat and mass transfer. The third theme investigates condensation in NPP by exploring passive cooling systems and condensation phenomena under both natural and forced convection conditions during nuclear accidents, the origin of NCGs in NPP and their transportation aspects. This is followed by experimental work related to condensation scenarios and scale. Finally, the last theme looks upon the recent advancements in computational fluid dynamics (CFD) modelling of wall condensation, system analysis codes coupling with CFD, and the implementation of machine learning (ML) for predicting the condensation HTC. By bridging the gap between fundamental knowledge and practical applications, the four thematic areas presented in this work are aimed at providing a comprehensive foundation for researchers and experts in the field of steam condensation when NCGs are involved. The ultimate objective is to bolster the safety and efficacy of NPP operations by understanding the heat and mass transfer mechanisms while mitigating the risk of catastrophic events.

A nuclear power plant digital twin for developing robot navigation and interaction

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.