Safety Of Nuclear Power Plants Research Articles

Semantic Web-Based Identification of Key Causative Factors and Chains for Team Error in NPPs' ACR

Team errors are identified as a major contributor to incidents in safety-critical systems like nuclear power plants (NPPs). Safety in NPPs is more dependent on team performance than individual, and reducing team errors requires identifying key causative factors and chains. Current studies primarily use qualitative analysis whereas lacking quantitative methods. This study addressed this by constructing a complex network model of team error based on semantic features and coding of the Semantic Web and determining criteria for selecting key causative factors by three node failure strategies. The top 5 team error key PSFs and top 5 high-risk causative chains were identified in the results. Finally, a set of control strategies was proposed further. This research can guide identifying key causative factors which could be a good starting point to scrutinize the team performance and establish control strategies for team error in NPPs.

Rotor fault diagnosis of centrifugal pumps in nuclear power plants based on CWGAN-GP-CNN for imbalanced dataset

As a crucial device in nuclear power plants, centrifugal pumps undertake the critical role of cooling water circulation. Centrifugal pump rotor misalignment and unbalanced faults cause pump performance degradation, vibration increase, and equipment damage, thus seriously affecting the safety and reliability of nuclear power plants. In the process of centrifugal pump rotor fault, the difficulty in obtaining data samples and the limited amount of data can lead to an imbalance problem between the quantity of normal state and fault state samples in the dataset. In order to solve the problem, this paper proposed a CWGAN-GP model for generating rotor fault data based on CGAN and WGAN-GP models, and combined it with a two-stream CNN model to realize the rotor fault diagnosis with an imbalanced dataset. The quality and performance of the data generated by the proposed method were evaluated and validated in terms of visualization analysis, statistical indicators, and comparison with different data generation models. The results show that the CWGAN-GP model can generate high-quality data. Meanwhile, compared with other models on datasets with different degrees of imbalance, the two-stream CNN model is more effective in fault diagnosis on the expanded dataset by the CWGAN-GP model, and the improvement of fault diagnosis accuracy ranges from 1.40% to 13.33%.

A Novel Ultrasonic Leak Detection System in Nuclear Power Plants Using Rigid Guide Tubes with FCOG and SNR.

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments.

Safety, Security, and Design Consideration of Nuclear Power Plants in the Kingdom of Saudi Arabia

Safety, Security, and Design Consideration of Nuclear Power Plants in the Kingdom of Saudi Arabia

Advanced Sobol sensitivity analysis of a 1:4-scale prestressed concrete containment vessel using an ANN-based surrogate model

The safety of nuclear power plants can be enhanced by performing sensitivity analyses to identify and evaluate factors that significantly impact their structural integrity. In this study, an artificial neural network-based surrogate model was developed using an experimentally validated finite element model. A Sobol sensitivity analysis was performed using this model to quantitatively assess the impact of material uncertainty on the internal pressure–displacement behavior of containment buildings at various internal pressure levels. The results indicate that the compressive strength of concrete and the prestressing force in the hoop direction critically influence the behavior of containment buildings, with their importance varying according to the internal pressure level. Furthermore, the proposed surrogate model can efficiently substitute for expensive and complex finite element analyses, enabling effective exploration of the multidimensional uncertainty space and quantitative assessment of the major factors affecting structural behavior. The process presented herein facilitates the quantification of factors under various uncertainty scenarios.

Research on Internal Flooding Analysis of Small Modular Reactors Based on Particle Method

Abstract The analysis of internal water flooding in nuclear power plants is a necessary part of safety review in the approval process of nuclear power plant construction projects. According to relevant regulations, it is necessary to conduct water flooding spread analysis on pipelines, water tanks, or pools filled with liquid in nuclear power plant buildings, analyze the spread path and water flooding height. This article analyzes and calculates the flow characteristics of water in a nuclear power plant building using a fluid calculation method based on the Lagrangian method (particle method). Taking the small modular reactor JR building as a case study, it analyzes the spread and height of four water flooding sources inside the building. According to the simulation time of 1800S, the results show that the flooding caused by the No. 2 water source is the most severe, with a flooding height of 1.367m. Drainage or preventive measures need to be taken to prevent the pipeline from breaking accidents; The flooding sources 1, 2, and 4 have a relatively mild impact, resulting in a lower flooding height and will not have a significant impact on important safety equipment, with little impact on the safety of nuclear power plants. The CFD analysis method was used to conduct water flooding analysis for nuclear power plants, which has the characteristics of strong adaptability and good visualization. It can dynamically simulate the entire process of water flooding analysis and better guide the design of water flooding protection for nuclear power plants.

Assessment of Power Equipment Operational Safety in the Sustainable Management of Residual Lifespan

Introduction. Ensuring the safe operation of energy facilities is a critical issue at all life cycle stages. It is associated with the adoption of management decisions when rescheduling and assessing the remaining resources, which are made based on a comprehensive assessment of the technical condition of the equipment, taking into account the economic efficiency and environmental requirements for further operation, which is the basis for technical and economic assessments. Aim and tasks. This study aims to develop a technical and economic assessment of the safe operation of power equipment to ensure sustainable management of the remaining service lifespan. Results. An algorithm for assessing pipeline system elements by the coolant environment is proposed, which allows for the pipeline load and changes in erosion and corrosion caused by coolant movement. It was proposed that the equipment be systematised by safety class and coolant. When conducting a technical and economic assessment, these parameters and the costs of extending the service life and upgrading the existing equipment were considered in the standard calculations of a certain type of equipment. For this purpose, mathematical models for calculating the residual life for each type, obtained and confirmed based on empirical studies, are proposed. Research findings into ensuring the reliability and safety of operation with various heat carriers have shown that it is necessary to consider low-cycle and high-cycle loads and erosion processes. It was found that the erosive wear rate is 0.3-0.4 mm/year for bending sections of the main circulation pipeline, where maximum loads occur relative to low-cycle and vibration loads, and the stress is 212.5 MPa when assessing the residual life of 7 years. Conclusions. The cost-effectiveness of reassigning the service life is reasonable, as it does not require additional investment in upgrading or replacing equipment subject to annual technical diagnostics. The systematisation of pipelines, improved codifiers, lifespan assessment models, and algorithms for evaluating the remaining life under various loadings enhance the regulatory framework for safe nuclear power plant operations and support technical and economic evaluations for extending their lifespan.

Very high cycle fatigue of austenitc stainless steels and their welds for reactor internals at ambient trmperature and 300 °C

The fatigue assessment of safety relevant components is of importance for ageing management with regard to safety and reliability of nuclear power plants. Austenitic stainless steels are often used for reactor internals due to their excellent mechanical and technological properties as well as their corrosion resistance. During operation reactor internals are subject to mechanical and thermo-mechanical loading which induce low cycle (LCF), high cycle (HCF) and even very high cycle (VHCF) fatigue. While the LCF behavior of austenitic steels is already well investigated the fatigue behavior in the VHCF regime has not been characterized in detail so far. Accordingly, the fatigue curves in the applicable international design codes have been extended from originally 106 to the range of highest load cycles up to 1011 load cycles by extrapolation. Nevertheless, the existing data base for load cycles above 107 is still highly insufficient. The aim of the cooperative project of the Institute of Materials Science and Engineering (WKK) at RPTU Kaiserslautern-Landau, Materials Testing Institute (MPA) Stuttgart and Framatome GmbH, Germany is to create a comprehensive database up to the highest load cycles N = 2·109 for austenitic stainless steels and their welds at ambient and elevated temperature.

Study on the effects of accident-tolerant fuels on the safety of CPR1000 nuclear power plants

Abstract In this paper, CPR1000 Nuclear power plant was taken as the reference unit. According to CPR1000 Probabilistic Safety Analysis (PSA), the Large Break loss of coolant accident (LOCA), Medium Break LOCA, Small Break LOCA, Station Blackout (SBO), Total Loss of Feed water (TLOFW) and Anticipated transient without trip (ATWT) with Loss of Main Feed water (LOMF) were selected as the representative accident scenarios. The house holding deterministic programs LOCUST were used to analyze each of the above selected scenarios for five different accident tolerant fuels (ATF). The effects on accident process, reactor core damage time, system success criteria and personnel response time were analyzed under the above scenarios. The above results for the five ATF materials were compared to those for standard UO2-Zr in the same scenarios, then some useful conclusions were obtained which can be used to support ATF material selection. In the end, based on the deterministic calculations, the effects of different ATF materials on CPR1000 PSA results such as core damage frequency (CDF) were evaluated in detail with traditional PSA software Risk Spectrum. According to the results, ATFs have some advantages on core damage time, success criteria or operators’ action time window. But the CDF was changed little compared to standard UO2-Zr system. Through the studies, it is found the most important thing is that due to the much longer time windows some new plant modifications can be provided to mitigate the accidents which can contribute to the plant safety and decrease risk more.

Assuring fire safety in nuclear plants with international standards

This paper presents the evolution and benefits of international standards for fire safety engineering11Fire safety engineering and performance-based fire safety are both widely used, and both represent an engineering process to determine performance of a fire protection plan based on the results of fire calculations. developed at the International Organization of Standardization (ISO). The history of fire safety in nuclear power plants along with the need for fire safety regulation is discussed to lay the foundation for the standards development work. The evolution of fire safety regulation from prescriptive to performance-based is also presented as the background for the research.Extensive research of the issues that arise for performance-based fire safety regulation, specifically for computer models for fire safety calculations, which included international benchmark exercises formed the basis to identify the need for developing international standards for fire safety engineering. The research focused both on the verification and validation (V&V) of computer fire models and determining their predictive capability. The paper for the first time takes new scientific and engineering data from benchmark exercises to assess the suitability and applicability of computer fire models for application in nuclear power plants. The paper reports on the findings of the benchmark exercises and their use in formulating new requirements in ISO international standards on verification and validation of fire calculation methods and the fire safety engineering process through a consensus process of international experts.Discussion of the foundational research at the ISO fire safety engineering committee (ISO TC 92 SC 4) led to a consensus to revise two international standards. One specifying the principles and general requirements for fire safety engineering, and another on the verification and validation procedures for fire calculation methods. The development and content of these two international standards are discussed in this paper. An international technical report was subsequently developed at the ISO committee for conformity assessment (CASCO) to provide guidance for the certification of the fire safety engineering process, and specific parts of it, namely the V&V of fire calculation methods.It is concluded that the fire safety engineering international standards presented in this paper can support rigorous performance-based fire safety regulation for nuclear power plants. These ISO international standards will be particularly useful to nations that have not yet developed the regulatory infrastructure for nuclear power regulation. The international standards are also available to those that would like to transition to the use of international standards and an ISO standards-based certification scheme. Regulations based on these international standards will provide citizens with confidence in nuclear power as a viable option to address climate change.

A condition-informed dynamic Bayesian network framework to support severe accident management in nuclear power plants

In the nuclear industry, Severe Accident Management Guidelines (SAMGs) are developed with the objective of providing prescriptive actions for mitigating the consequences of Nuclear Power Plant (NPP) accidents. These guidelines are defined with respect to a set of expert-based prototypical severe accident scenarios and are symptom-based, i.e., they relate to the values of specific monitored plant parameters. Expert-based SAMGs might fail to deal with non-prototypical scenarios, i.e., scenarios not considered a priori by the experts. To address this issue, in this paper we propose a methodology based on Dynamic Bayesian Networks (DBNs) to support operators decision-making for avoiding the escalation of severe accidents to non-prototypical scenarios. The methodology is based on the integration of condition monitoring data into a DBN to allow it to i) infer the current Plant Operating Condition (POC), Initiating Event (IE), and NPP safety barriers Damage State (DS), ii) predict the grace time (GT) available to carry out mitigative actions, and iii) evaluate the effectiveness of the alternative candidate mitigative actions taking into account the safety barriers estimated DS and the predicted available GT. The proposed methodology is exemplified on a Loss of Coolant Accident (LOCA) in a WWER-1000.

Numerical modeling of discharge modes and evaluation of the major characteristics of latent heat thermal energy storage at a nuclear power plant

Under conditions of uneven power consumption schedules, construction of renewable power plants and low maneuverability of nuclear power plants (NPPs), combining NPPs with latent heat thermal energy storage systems (LHTES) can improve efficiency and maneuverability of NPPs. Combination of the LHTES with an additional low-capacity steam turbine will allow not only meeting peak electricity loads, but also increase the NPP safety by additional reservation of its own needs using this multifunctional turbine in emergency situations of complete blackouts. A scheme for combining a PCM-based thermal storage system with a two-circuit nuclear power plant is proposed, where the LHTES is charged with part of fresh steam from steam generators in the night hours of off-peak loads in the power grid and heating feed water above the nominal temperature during the peak loads. Non-stationary heat transfer between the heat storage material and feed water during the LHTES discharge was modeled using the finite volume method. The design characteristics of the LHTES as part of the NPP and the weight-size parameters of the thermal accumulator have been determined. The rational phase change material and the types of finned tubes have been determined. A solution has been proposed to ensure stable water temperature at the outlet of the PCM-based thermal accumulator during its discharge within the NPP that will significantly increase efficiency of its performance. Heat losses from the LHTES to the environment during the day have been calculated.

Risk-based method for substantiating reduction of pressure of hydraulic strength tests and its application on steam line of nuclear power plant with WWER (PWR) reactor plant

Hydraulic tests are used in various sectors of the national economy to check the strength and tightness of vessels and pipelines, their parts and assembly units loaded with pressure. The hydraulic test pressure is a calculated value and is determined on the basis of regulatory documentation for the relevant control objects. Analysis of current trends in the field of hydraulic testing both in the Russian Federation and abroad has shown a trend to reduce the damageability of test objects by choosing the most optimal hydraulic test pressure. The main purpose of the research was to reduce the operational damage to equipment and pipelines of nuclear power plants by reducing the pressure of hydraulic strength tests during maintenance. The research is based on probability theory, mathematical statistics, strength physics and fracture mechanics, taking into account the possibility of occasional discontinuities or defects that might be observed in operation. The method is based on a risk-based approach, which makes it possible to determine the permissible risk variation based on recommendations on the safety of nuclear power plants in the Russian Federation. The objects of research are the equipment and pipelines of nuclear power plants. The result of the research is the development of a method substantiating the reduction of the pressure of hydraulic strength tests for equipment and pipelines of nuclear power plants based on a risk-based approach. The developed method can be used by organizations involved in the production of electricity (at nuclear or thermal power plants), transportation of oil, gas or other substances, at facilities where pipelines are an essential part of their structures, affecting the reliability and efficiency of operation of these facilities.

A computationally efficient approach of tuned mass damper design for a nuclear cabinet based on two-step machine learning and optimization methods

Enhancing nuclear power plant (NPP) safety is demanded because of the recent beyond-design-basis earthquake near a NPP. Therefore, research on improving the seismic performance of the electrical cabinet, which ensures the safe operation of NPPs, is needed. In this paper, a tuned mass damper (TMD) is employed to control the seismic response of cabinet. To design the TMD, we employ existing design equations or perform numerical model–based optimization. However, limitations, such as inconsistencies with targeted control of the load and structure, the possibility of converging a local solution, and the high cost of numerical analysis. Therefore, this paper proposes a two-step machine learning and optimization method. Such an approach is utilized to find the optimal global design solution and reduce numerical analysis costs. Each step involves the design of experiment (DOE), response surface, and optimization. Notably, range setting in the DOE accounts for the difference between each step. In the first step, the sampling range is widened to determine the relationship between the design variables and the cabinet's response, and in the second step, the sampling range is narrowed depending on the result of the first step. Consequently, the proposed method reduced the cabinet's response by 35.4 % on average and numerical analysis cost declined by 1/3.

A case study to address the limitation of accident scenario identifications with respect to diverse manual responses

Probabilistic safety assessment (PSA) is a tool for securing the operational safety of nuclear power plants. One unique benefit of PSA is to identify accident scenarios leading to undesirable consequences. Since these accident scenarios result from highly complicated combinations of automatic and manual responses, a huge number of simulations are generally required with a precise thermal-hydraulic (TH) code. Therefore, current PSA typically reduces the number of TH code runs by focusing on limited numbers of safety-critical components with reasonable engineering assumptions. Nevertheless, it has been pointed out that the current catalog of accident scenarios in PSA may be insufficient to some extent. In order to corroborate this claim, in this work, 14,348,907 simulation conditions were randomly generated after combining 79 manual responses identified from the emergency operating procedure of a steam generator tube rupture (SGTR) event in a reference nuclear power plant. Results show that about 14.7 % of the simulations correspond to an undesirable consequence pertaining to the SGTR. Accordingly, in terms of enhancing the quality of PSA results, the results of this study support that it is inevitable to develop a promising way to reduce the amount of computational resources required by large numbers of TH code runs.

Phase-Field Simulation of Grain Growth in Uranium Silicide Nuclear Fuel

Uranium silicide (U3Si2) is regarded as a viable fuel option for improving the safety of nuclear power plants. In the present work, phase-field simulations were employed to investigate grain growth phenomena, encompassing both isotropic and anisotropic grain growth. In simulations of isotropic grain growth, it is commonly assumed that the energy and mobility of the grain boundaries (GBs) remain constant, represented by average values. The calculated grain growth kinetic rate constant, K, exhibits a close correspondence with the experimental measurements, indicating a strong agreement between the two. In simulations of anisotropic grain growth, the values of GB energy and mobility are correlated with the angular disparity between GBs. The simulation results demonstrated that the growth rate of U3Si2 can be influenced by both the energy anisotropy and mobility anisotropy of GBs. Furthermore, the anisotropy in mobility results in a greater prevalence of low-angle GB distribution in comparison to high-angle GBs. However, the energy anisotropy of GBs does not impact the frequency distribution of the angle difference between GBs.

Experimental and numerical study on the seismic response reduction effect of TMD on nuclear cabinets using shaking table test

Improving the seismic performance of equipment is required for safety of nuclear power plants (NPPs). In particular, electric cabinets are installed in multiple places of NPP and include electronic devices inside. Thus, damage to the cabinet under earthquakes can adversely affect the NPP safety and operation. This study conducts a real-scale shaking table test and experimentally confirms the seismic response reduction effect of a tuned mass damper (TMD) on the NPP cabinet. Specifically, the shaking table test identifies the cabinet dynamic characteristics first. Then, the TMD device is designed and manufactured, and the seismic and dynamic responses according to the presence of the TMD are compared and analyzed through the real-scale shaking table test. Also, the effect of such a TMD is experimentally investigated even when the cabinet dynamic characteristic is changed. Finally, a numerical model is derived based on these test results. As a result, it is confirmed via the test that the TMD decreases seismic responses of the cabinet by about 14–31 %. Also, such a TMD decreases seismic responses of the cabinet by 14–25 % even if the target cabinet dynamic characteristic is modified. Finally, the numerical models are well created and validated for further investigation on multiple cases.

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.

Simulation study on the dynamic characteristics of natural circulation coupled phase change material for equal height difference passive containment cooling system

The passive containment cooling system (PCCS) plays a significant role in ensuring the safety of nuclear power plants. To further reduce peak pressure in the containment during a loss of coolant accident (LOCA), phase change materials (PCMs) are utilized to improve the system’s cooling effectiveness. The dynamic simulation model of PCCS has been developed using Apros software. This article focuses on examining the system’s dynamic characteristics related to various tube startup methods and comparatively analyzing the impact of different PCM thicknesses on heat transfer capacity. The results indicate that post-accident, PCM significantly suppresses peak pressure in the containment. Under the full tube startup method with 4 mm PCM, the peak pressure decreases to 1.75 MPa, whereas under the empty tube startup method, it is reduced to 1.69 MPa. With natural circulation empty tube startup method, PCM can reduce pressure change intensity in the containment but negatively affects natural circulation heat transfer. Although the full tube startup method underperforms the empty tube startup method on suppressing peak pressure, PCM can positively affect natural circulation heat transfer in the early stages of the accident. Empty tube startup offers the advantages of higher heat transfer capacity and lighter weight, making it the optimal choice for ocean nuclear platforms. Additionally, the higher peak power of PCM heat transfer under the empty tube startup method indicates that system can withstand larger thermal shocks.

Sensor degradation in nuclear reactor pressure vessels: the overlooked factor in remaining useful life prediction

Sensor degradation poses a critical yet ‘often overlooked’ challenge in accurately predicting the remaining useful life (RUL) of nuclear reactor pressure vessels (RPVs), hindering safe and efficient plant operation. This paper introduces an approach to RUL estimation that explicitly addresses sensor degradation, a significant departure from conventional methods. We model neutron embrittlement, a dominant degradation process in RPV steel, as a Wiener process and leverage real-world surveillance capsule data for insightful parameterization. Maximum likelihood estimation is utilized to characterize the degradation dynamics in the model. A Kalman filter then seamlessly integrates the degradation model with sensor measurements, effectively compensating for degradation-induced errors and providing refined state estimates. These estimates power a robust RUL prediction framework. Our results expose the profound impact of sensor degradation on conventional RUL predictions. By directly confronting sensor degradation, our method yields substantially more accurate and reliable RUL estimates. This work marks a significant advancement in the field of materials degradation, offering a powerful tool to optimize nuclear power plant safety and longevity.