Safety Of Nuclear Power Plants Research Articles

CFD Analysis of Natural Convection on the Outer Surface of the Containment of APWR Model

To improve the safety of nuclear power plants, a modern NPP such as Advance Pressurize Water Reactor (APWR) is equipped with a safety feature called PCCS (Passive Containment Cooling System), where naturally circulating air cools the outer surface of the containment which is used to remove decay heat released inside the containment vessel. The decay heat from the reactor core will be transferred out through the conduction mechanism and the natural convection mechanism on the outer surface of the containment vessel. Then, the hot air that forms in the gap between the containment and its baffles will rose through the chimney located at the top of the concrete building and cold air will enter through the inlet to create a natural circulation cycle. To obtain the effectiveness of the use of baffles, as a comparison, it is necessary to analyze the natural convection heat transfer on the outer surface of a containment vessel that is not equipped with a baffle. In this research, CFD analysis of natural convection on the outer surface of the containment of APWR reactor model has been done. Based on the model developed, the analysis was done to get temperature and convection heat transfer coefficient in the air flow on the containment surface. Heat flux in the containment surface varied from 500 W/m2 to 2.000 W/m2 with an increase of 500 W/m2 intervals. Based on the analysis results, the correlation equations are also proposed in this paper, namely the correlation of natural convection heat transfer for laminar regime on a vertical cylindrical with the heat flux constant in the form Nuq = a(Raq)b.

Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Abstract The digitalization enhances nuclear power plant (NPP) instrumentation and control (I&C) system performance, while simultaneously introducing safety and reliability challenges. To enhance the stability of distributed control system (DCS), and minimize the dangerous failure rate and safe failure rate of safety system, signal quality bits (SQBs) are widely used in NPP DCS to identify the validity of important signals. However, in the past decade or so, numerous unscheduled turbine shutdowns and/or reactor trips have resulted from incorrect SQB design or settings. Therefore, it is necessary to conduct optimization research on the design methods and setting principles of SQBs, establish an optimization method for DCS application software SQB verification and validation (V&V), then minimize DCS faults attributed to SQB. With the aim, this work conducts an in-depth investigation and analysis of the definition, design, setting and transmission of SQB in NPP. A specific optimization method for software V&V to conduct SQB on NPP safety DCS application software is proposed. Twelve concerns in the SQB V&V work are detailed. The reactor trip system ΔT protection logic is utilized as a case to illustrate the detail implementation process of this optimization method. This work provides an engineering technical reference and operational guidance for SQB software V&V, conducive to ensuring the safe and economical operation of NPPs.

Development of an AI-based remaining trip time prediction system for nuclear power plants

In abnormal states of nuclear power plants (NPPs), operators undertake mitigation actions to restore a normal state and prevent reactor trips. However, in abnormal states, the NPP condition fluctuates rapidly, which can lead to human error. If human error occurs, the condition of an NPP can deteriorate, leading to reactor trips. Sudden shutdowns, such as reactor trips, can result in the failure of numerous NPP facilities and economic losses. This study develops a remaining trip time (RTT) prediction system as part of an operator support system to reduce possible human errors and improve the safety of NPPs. The RTT prediction system consists of an algorithm that utilizes artificial intelligence (AI) and explainable AI (XAI) methods, such as autoencoders, light gradient-boosting machines, and Shapley additive explanations. AI methods provide diagnostic information about the abnormal states that occur and predict the remaining time until a reactor trip occurs. The XAI method improves the reliability of AI by providing a rationale for RTT prediction results and information on the main variables of the status of NPPs. The RTT prediction system includes an interface that can effectively provide the results of the system.

Ensuring Nuclear Safety in Ukraine in Conditions of War

The ongoing war in Ukraine has significantly raised concerns about the safety of nuclear power plants (NPPs), especially in the context of the Zaporizhzhia NPP. This precarious situation has revealed substantial weaknesses in the existing global nuclear safety framework, emphasizing the need for sweeping reforms in international law. While the International Atomic Energy Agency (IAEA) has played a commendable role in advocating for nuclear safety through various resolutions, missions, and safety principles, the effectiveness of these efforts is compromised by Russia’s non-compliance and its ability to exercise veto power in the UN Security Council. Confronting these challenges requires a diverse and comprehensive approach from the international community. This includes revising international legal structures, conducting in-depth studies on nuclear safety in conflict zones, and considering diplomatic initiatives like establishing safety zones around nuclear facilities. Another possible strategy is the deployment of peacekeeping forces, though geopolitical factors currently limit its viability. Resolving the nuclear safety concerns intensified by the Russia-Ukrainian war demands robust legal and political actions, coupled with innovative solutions and global cooperation. Immediate and focused attention is crucial to avert any further escalation that risks a nuclear incident, posing dire threats not only to Ukraine but to the entire international community.

Numerical study on surface corrosion deposition of fuel elements and its influence on flow heat transfer

Corrosion of pressurized water reactors (PWR) in nuclear power plants can lead to serious safety hazards. This study aims to analyze the deposition of corrosion products using FLUENT software. Deposition models and thermal resistance models were developed, and the effects of deposits on the reactor’s thermal–hydraulic characteristics were evaluated. Additionally, the impact of various parameters on deposition and thermal–hydraulic characteristics was examined. Results show that deposits accumulate extensively in the inlet section of the fuel cladding, while appearing as spot deposits in the outlet section. For deposit thicknesses below 30 μm, the surface temperature of the cladding gradually increases. However, when the thickness exceeds 30 μm, the surface temperature rapidly rises. Furthermore, the study reveals that the deposition amount decreases with increasing inlet flow velocity, exhibits an upward trend with higher inlet temperature, and increases with a higher wall heat flux density. This research provides important insights for understanding core deposition and thermal–hydraulic characteristics in nuclear reactor systems. It offers valuable guidance for enhancing safety and operational efficiency in nuclear power plants.

Правовые вопросы безопасности АЭС в зоне военных действий

The article discusses the legal aspects of physical protection and nuclear safety of nuclear power plants located in a combat zone. The existing multilateral international treaties in this area, namely, the 1979 Convention on the Physical Protection of Nuclear Material (CPPNM) and the 2005 Amendment to the 2005 Convention on the Physical Protection of Nuclear Material, do not regulate the physical protection of nuclear facilities located in areas of active combat using conventional weapons. Moreover, the IAEA soft law provisions do not regulate the issues of physical protection of nuclear power plants located in areas of full-scale combat operations. The situation with the Zaporizhzhia Nuclear Power Plant has made the international community and the IAEA face the issue of legal regulation and development of recommendations for the physical protection of nuclear facilities in a war zone. Currently, a new concept for the physical protection of the Zaporizhzhia Nuclear Power Plant is being discussed under the auspices of the IAEA and efforts to prepare a new international treaty on the physical security of nuclear facilities in a war zone are underway.

Enhancement in the safety and reliability of Pressurized Water reactors using Machine Learning approach

The increasing interest in artificial intelligence and automation within the nuclear industry stems from the hope of elevating the safety and reliability of nuclear power plants by minimizing the impact of human errors. This paper explores the capabilities of machine-learning based fault detection and diagnosis (FDD) models in accurately classifying transient events in a nuclear power plant using the Classification Learners Application from MATLAB. The scope of the research is narrowed to the identification of transient events of the same nature occurring at various locations within the plant. To accomplish this, the Generic Pressurized Water Reactor (GPWR) from the Western Services Cooperation (WSC) was used to simulate 14 transients in the primary system and 10 transients in the secondary system, resulting in two distinct datasets of 108,000 and 79,200 observations. Nine types of classifiers, including Decision Trees, Discriminant Analysis, Support Vector Machines, Logistic Regression, Nearest Neighbors, Naive Bayes, Kernel Approximation, Ensembles, and Neural Networks, with a total of 33 predefined models, were trained and validated. The K-Nearest Neighbors model achieved the highest accuracy of 97% in the primary system, while the Efficient Linear Discriminant and Logistic Regression models achieved the highest average accuracy of 99% in the secondary system. The assessment of the top-performing models was conducted through a comprehensive analysis employing key classification model evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and the F1 score. In an effort to optimize the models, the study integrated feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm. Additionally, the investigation explored different validation schemes, including both holdout and k-fold cross-validation schemes, resulting in a substantial reduction in training time without significantly compromising the overall accuracy of the models. The optimized models in this study demonstrated remarkable prediction accuracies, consistently exceeding 94%. Despite the complexity of the models and the intricacies involved in the training process, the training times ranged from 10 to 1800 s, reflecting the efficiency and robustness of the implemented optimization techniques. The results of this study underscore the potential of machine learning models in identifying transient events in nuclear reactors, showcasing their promising capabilities while maintaining low computational and execution costs. This suggests their efficacy in optimizing safe and efficient operational practices within nuclear facilities.

Deep learning for predicting the residual concentration of sodium hypochlorite in the cooling water OF nuclear power plants

Sea water is used as cooling water in several operating nuclear power plants, as well as in other industries such as petrochemicals. Biofouling is a common problem in systems that use sea water, causing corrosion in pipes and equipment, blockages, and loss of efficiency in heat exchangers. The use of sodium hypochlorite has proven to be effective in minimizing the damages caused by biofouling, provided that the residual chlorine concentration remains within a specific range, as low dosages will not protect the equipment and high dosages can cause environmental damage. With the aim of reducing maintenance and operation costs of equipment, and particularly ensuring the operability of systems related to the safety of nuclear power plants that use sea water, this study developed deep artificial neural networks (ANN) models capable of predicting the residual chlorine concentration in the main cooling and safety systems of a nuclear power plant. This prediction is based on environmental and process parameters that influence the residual chlorine concentration. Proposed model achieved average mean squared error of 0.002 and correlation coefficient (R2-Score) of 0.934, demonstrating to be a promising tool for predicting residual chlorine concentration.

Dimensional synthesis of an Inspection Robot for SG tube-sheet

To ensure the operational safety of nuclear power plants, we present a Quadruped Inspection Robot that can be used for many types of steam generators. Since the Inspection Robot relies on the Holding Modules to grip the tube-sheet, it can be regarded as a hybrid robot with variable configurations, switching between 4-RRR-RR, 3-RRR-RR, and two types of 2-RRR-RR, and the variable configurations bring a great challenge to dimensional synthesis. In this paper, the kinematic model of the Inspection Robot in multiple configurations is established, and the analytical solution is given. The workspace mapping is analyzed by the solution-space, and the workspace of multiple configurations is decomposed into the workspace of 2-RRR to reduce the analysis complexity, and the workspace calculation is simplified by using the envelope rings. The optimization problem of the manipulator is transformed into the calculation of the shortest contraction length of the swing leg. The switching performance of the Inspection Robot is evaluated by stride-length, turning-angle, and workspace overlap-ratio. The performance indexes are classified and transformed based on the proportions and variation trends of dimensional parameters to reduce the number of optimization objective functions, and Pareto optimal solutions are obtained using an intelligent optimization algorithm.

Responses of microeukaryotic community structure to a Phaeocystis globosa bloom in a semi-enclosed subtropical bay

The occurrence of Phaeocystis globosa, a harmful algal bloom species in Chinese coastal waters, has significant impacts on marine organisms and poses a threat to the safety of coastal nuclear power plants. Although previous studies have established a close association between P. globosa blooms and the bacterial community, the relationship between the microeukaryotic community and P. globosa blooms remains poorly understood. In this study, the variations in the microeukaryotic community resulting from a P. globosa bloom were analyzed using 18S rRNA gene amplicon sequencing. The results indicated that the diversity of the microeukaryotic community during the bloom phase was significantly higher than that during the dissipation phase. The microeukaryotic community compositions varied significantly between the two phases of the P. globosa bloom. During the bloom phase, the dominant microeukaryotic was Viridiplantae, which was then replaced by Dinoflagellata during the dissipation phase. Co-occurrence network analysis showed that the relationship among the microeukaryotic community during the bloom phase was more complex than that during the dissipation phase, and the keystone taxa varied as the bloom progressed. Additionally, microeukaryotic community assembly was primarily driven by stochastic processes during the bloom phase based on the β-nearest taxon distance, whereas it was driven by both deterministic processes and stochastic processes during the dissipation phase. Overall, our findings provide novel insight into the mechanisms and interactions involved in microeukaryotic community dynamics in environments disturbed by P. globosa blooms.

Atomic blackmail – Russia-Ukraine war and Ramberg’s theory of vulnerability

ABSTRACT The Russia-Ukraine War is being fought between states committed to nuclear power. This paper references Ramberg’s nuclear powerplant (NPP) vulnerability treatise, the IAEA’s Seven Pillars of Safety metric and Pidgeon and O’Leary’s safety imagination approach to assess the safety of Ukrainian and Russian NPPs. It suggests governments should reflect on events in Ukraine, and, with reference to the above-mentioned approaches, decide whether the upside of nuclear power – the largely carbon-neutral production of electricity – outweighs the downside – giving a blackmail opportunity to hostile states. Although a cost-benefit analysis to inform decision-making is effortful, the paper suggests that failure to consider every advantage and disadvantage of nuclear power could prove costly in human and environmental terms. Drawing on Ukraine’s experience, the paper suggests the national interest is best served in time of war by generating electricity in multiple small plants rather than a few large plants.

Investigating Natural Disaster‐Related External Events at Nuclear Power Plants: Towards Climate Change Resilience

Climate change is causing rapid shifts in the intensity and frequency of natural disasters worldwide, which may have implications for the safety of nuclear power plants. As these natural disasters become more frequent and severe, their potential impact on nuclear facilities is expected to increase. Several instances of nuclear power plants being forced to shut down due to climate change have already been observed. The changing patterns of natural disasters can lead to alterations in external events affecting nuclear power plants. Therefore, it is crucial to investigate how climate change could affect the safety and operations of nuclear power plants in order to establish protective measures and mitigation strategies. This study examined external events associated with climate change that could potentially impact nuclear power plants, and both Korean and international cases were summarized. Recommendations for future tasks were proposed based on these findings. Currently, there have been no significant reported cases where climate change has severely impacted the operations and safety of nuclear power plants. However, it is essential to undertake periodic evaluations, long‐term management, and further research to ensure the safety and continued operation of nuclear power plants in the face of climate change.

Compilation of the Job Competencies Model of the Parent Company Specialized in the Production and Development of Atomic Energy in Iran

Objective: The aim of this study is to develop a competency model for jobs at the Bushehr Nuclear Power Plant, a subsidiary of the Atomic Energy Organization of Iran. Methodology: This mixed-methods research utilized both qualitative and quantitative approaches. Initially, qualitative data were collected through library studies and thematic analysis. Based on this data, a questionnaire was designed to gather quantitative information, which was distributed among 90 employees and managers of the Bushehr Nuclear Power Plant. The collected data were then analyzed using structural equation modeling. Findings: The results of the analysis showed that the proposed competency model had a good fit, with all structural paths being statistically significant. Both technical and managerial competencies played a crucial role in improving employee performance and safety. The model’s goodness-of-fit index was calculated at 0.413, indicating a strong fit. Conclusion: This study concluded that developing a multidimensional competency model, including technical, managerial, and communication skills, can lead to improved efficiency and safety in nuclear power plants. Implementing such models in recruitment, training, and employee evaluation processes can positively impact organizational productivity.

Zero-suppressed ternary decision diagram algorithm for solving noncoherent fault trees in probabilistic safety assessment of nuclear power plants

Probabilistic safety assessment (PSA) plays a critical role in ensuring the safe operation of nuclear power plants. In PSA, event trees are developed to identify accident sequences that could lead to core damage. These event trees are then transformed into a core-damage fault tree, wherein the accident sequences are represented by usual and complemented logic gates representing failed and successful operations of safety systems, respectively. The core damage frequency (CDF) is estimated by calculating the minimal cut sets (MCSs) of the core-damage fault tree.Delete-term approximation (DTA) is commonly employed to approximately solve MCSs representing accident sequence logics from noncoherent core-damage fault trees. However, DTA can lead to an overestimation of CDF, particularly when fault trees contain many nonrare events. To address this issue, the present study introduces a new zero-suppressed ternary decision diagram (ZTDD) algorithm that averts the CDF overestimation caused by DTA.This ZTDD algorithm can optionally calculate MCSs with DTA or prime implicants (PIs) without any approximation from the core-damage fault tree. By calculating PIs, accurate CDF can be calculated.The present study provides a comprehensive explanation of the ZTDD structure, formula of the ZTDD algorithm, ZTDD minimization, probability calculation from ZTDD, strength of the ZTDD algorithm, and ZTDD application results. Results reveal that the ZTDD algorithm is a powerful tool that can quickly and accurately calculate CDF and drastically improve the safety of nuclear power plants.

Proposal of a novel AI-based plant operator support system for the safety of nuclear power plants

Proposal of a novel AI-based plant operator support system for the safety of nuclear power plants

Relative humidity prediction of a leakage area for small RCS leakage quantification by applying the Bi-LSTM neural networks

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.

Optimization of prevention and mitigation of severe accidents through Research: Major outcomes and outlook of the Spanish programme

Severe accidents research activities in Spain have grown substantially since the Fukushima-Daiichi accident. Most of these research activities have been conducting by a few organizations (national research center and academia) with the support of the Regulatory Authority, utilities and engineering companies. A great number of these activities were conducted in the analytical field and their main outcomes are contributing to a better understanding of severe accident phenomena (i.e., pool scrubbing; combustible gas distribution and combustion; fission products transport; etc.) and the efficiency of severe accident management actions (i.e., FLEX; PARs; activation of safety systems, long-term measures; etc.). The national framework set between researchers and end-users (regulators and utilities) is improving the safety of the Spanish nuclear power plants and allows for the assimilation of the research results by the nuclear industry.

Interplay of human factors and safety culture in nuclear safety for enhanced organisational and individual Performance: A comprehensive review

The incidents at Three Mile Island and Chernobyl have poignantly spotlighted the indispensable role of human factors and organisational dimensions in ensuring nuclear safety. These events illuminated that beyond the intricate technicalities of nuclear operations, lie the equally significant non-technical facets that warrant utmost attention. While human factors delve into the cognitive and ergonomic challenges faced by operators, safety culture represents the ingrained organisational ethos that promotes or impedes safety standards. While a myriad of studies has dissected the impact of these elements on nuclear safety, the quest for an encompassing methodology that holistically integrates them is still ongoing. This paper embarked on an exhaustive review that probed the symbiotic relationship between human factors, safety culture, organisational performance, and individual performance within nuclear power plants, all the while bearing the indelible imprints of the Three Mile Island and Chernobyl disasters. Beginning with an elucidation of the core tenets of human factors and safety culture and their intrinsic ties to nuclear safety, the review delved deeper into empirical research that illustrated their influence on the overarching safety climate of nuclear establishments. It shines a light on recurrent challenges like communication lapses, leadership oversights, and the ever-present spectre of human error. In its culmination, the review underscored the intertwined nature of these pivotal elements and expounds on their collective import in sculpting a resilient safety culture in nuclear setups. Recommendations for bolstering human factors and safety culture practices, gleaned from the bitter lessons of Three Mile Island and Chernobyl, are proffered. At its core, this paper advocated for an integrative paradigm that melds organisational and individual performance metrics with human factors and safety culture insights, perpetually underscored by rigorous research and evaluation, to ensure the unerring safety and dependability of nuclear power plants.

Study of the impact of hydrides on the fracture toughness of Zr-2.5Nb alloy

Hydrogen absorption and hydride formation in zirconium (Zr) alloys is a well-known issue and has been extensively studied in the past. Hydrogen has an impact not only on the strength but also on the fracture toughness of Zr alloys. As various Zr alloys are used for manufacturing the core components of nuclear reactors, such as nuclear fuel claddings, fuel channels, fuel assemblies, fixtures, and etc., their structural integrity has a great impact on the safety and reliability of the whole nuclear power plant. Furthermore, the hydrogen absorption ageing mechanism retains its significance not just during reactor operation but also throughout decommissioning and storage of spent nuclear fuel, especially when sufficient levels of absorbed hydrogen concentrations are reached, potentially leading to a delayed hydride cracking mechanism. Assessing the impact of hydrogen absorption on the strength of zirconium alloys typically involves time-consuming, costly, and sample-intensive experimental studies. Consequently, there is a demand for alternative research approaches. This article introduces the methodology to evaluate the fracture toughness of the Zr-2.5Nb alloy with varying hydrogen concentrations.

Analysis of main steam line break accident on a BWR test facility using TRACE

PUMA-E is an integral test facility, which is used for extensive experimental investigations to study the behavior of Boiling Water Reactor (BWR) plants under accident conditions. The TRACE code is the best estimate thermal hydraulic system code developed by the U.S. Nuclear Regulatory Commission for performing nuclear power plant safety analysis. The aim of the present work is to conduct a short validation study of the TRACE code applied to the PUMA-E facility. Firstly, a TRACE model of the PUMA-E facility was developed based on the existing RELAP5 input deck. Then, an additional facility TRACE model with reactor pressure vessel modeled by three dimensional “VESSEL” component was also established. After that, a Main Steam Line Break accident test was simulated, and the calculation results were compared with experimental data and RELAP5 simulation results. The TRACE simulation capability to predict the accident phenomenon of BWR test facility was evaluated using both qualitative and quantitative methods. On the basis of the above research, the base case accompanying with one Gravity Driven Cooling System tank failure accident was carried out based on the three facility models. The validity and reliability of the simulation results of the different facility numerical models were mutually verified through a qualitative comparison of the simulation results.