Reliability Of Nuclear Power Plants Research Articles

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.

Condition monitoring for nuclear turbines with improved dynamic partial least squares and local information increment

Performing online condition monitoring for nuclear turbines in the rapidly changing environment is a challenging but imperative task to enhance the safety and reliability of nuclear power plants. Given the nonlinear and dynamic properties of nuclear turbine operation, this paper proposes an innovative method for condition monitoring. Specifically, the paper first redesigns time augmented matrices based on lagged data to reflect the process dynamics. Subsequently, a dynamic auto-regressive model, integrated with the variant of kernel partial least squares, is built between input and output variables, which represents auto-correlations and cross-correlations of operation data simultaneously. The prediction of the model serves as the baseline for the monitoring indicator. Additionally, since the operation process involves variable working excitation and random noise, making static control limits insufficient to satisfy the requirements of condition monitoring, the proposed method utilizes a novel monitoring indicator based on local information increment. The indicator comprehensively incorporates the prediction value and past measurement for monitoring statistics and control limits. Finally, the proposed method is applied to a real nuclear turbine operation process, and the results are compared with three other methods to demonstrate its superiority.

Pyrocarbon Coating on Granular Al2O3 for HTGR-Type Power Reactor

Fourth-generation nuclear power systems are based on high-temperature gas-cooled reactors, in which the pebble fuel is the primary energy carrier. In this regard, applying protective pyrocarbon coatings on granulated fuel is an essential problem in ensuring the reliability of nuclear power plants. The article’s main idea is to research rational technological parameters of forming a pyrocarbon protective coating on the granules of a nuclear fuel model. For this purpose, granulated Al2O3 with the protective pyrocarbone coating was applied as a fuel model. The article’s aim is to study the effect of thermophysical parameters on applying a protective pyrocarbon coating on granulated Al2O3. During the experimental studies, thermal imaging of the pyrolysis process was used. The scientific novelty of the work is the equilibrium curves for the systems Al2O3:CH4, Al2O3:CH4:N2, and Al2O3:CH4:Ar. Their analysis allowed for evaluating rational thermochemical parameters of the pyrolysis process. As a result, rational thermophysical parameters of coating granulated Al2O3 with a pyrocarbon layer were evaluated, and the practical possibility of applying the pyrocarbon coating to granulated Al2O3 in the electrothermal fluidized bed was experimentally proven. It was shown that nitrogen does not significantly affect the target reaction product under a temperature of less than 1500 K. Also, the rational conditions for the pyrocarbon coating at a pressure of 0.1 MPa were realized at a temperature of 900–1500 K and using argon. Moreover, pyrocarbon was precipitated from hydrocarbon at 1073–1273 K. Overall, the need to add an inert gas for reducing the carbon black formation was proven to prevent a reduction of natural gas efficiency.

Small Modular Reactors: Opportunities and Challenges as Emerging Nuclear Technologies for Power Production

Abstract Small Modular Reactors (SMRs) have gained international attention due to their modular design, small footprint, and lower capital costs for research, development, and construction compared to conventional reactors. Many types of SMRs are being developed in different countries, and regulatory agencies are working on a robust and “harmonized” SMR regulatory framework to ensure safety and protect the environment. However, there are still many details that need to be understood, such as nuclear fuel behavior at high pressures and temperatures (1000 °C), types and levels of radiation exposure during normal operations and accidents, types and volume of nuclear waste, and their proper storage and disposal. Moreover, SMRs' modular design and small size make them suitable for remote locations, such as the Canadian Arctic. However, before introducing this technology, a detailed study of the arctic soil (permafrost) is needed in the context of changing climate. Probabilistic risk assessment (PRA) is a crucial methodology for assessing the safety and reliability of nuclear power plants. Due to multi-module nature of SMRs, cross-unit interactions ( multi-module effects) need to be evaluated as part of the total plant safety assessment. Additionally, given the nature of fuels (low-enriched uranium) and the possible remote location with minimal technical staff, nuclear materials may have a higher probability of diversion. Therefore, nuclear safeguards and material accountancy are essential to prevent nuclear proliferation. This paper discusses the benefits and challenges of deploying different SMR technologies for electricity generation and other applications.

Sensitivity neutronic analysis of accident tolerant fuel concepts in APR1400

Zirconium-based cladding materials used in nuclear power plants have been shown to pose safety concerns due to their susceptibility to oxidation and hydrogen production during high-temperature conditions, such as loss-of-coolant accidents. In order to address these concerns, alternative cladding materials that are more resistant to these conditions are being explored. This study investigates the potential of using silicon carbide (SiC) and iron-chromium-aluminum (FeCrAl) cladding materials to improve the safety and efficiency of nuclear power plants, particularly in the context of accident tolerant fuel (ATF) technology concepts. The study utilized Serpent Monte Carlo 2.31 to conduct neutronics analyses on the APR-1400 reactor, assessing the impact of SiC and FeCrAl cladding materials on fuel reactivity, neutron spectrum, Pu-239 inventory, and pin power distribution. A sensitivity analysis was conducted by adjusting parameters such as fuel enrichment, cladding thickness, and pellet diameter to determine the effects of the new materials on design parameters. Results of the analysis revealed that, by adjusting design parameters, SiC and FeCrAl materials could be used to meet the cycle length of the original fuel-cladding (zircalloy). Furthermore, using SiC cladding resulted in a hardening of the neutron spectrum, which increased the production of actinides such as plutonium. By analyzing the effects of changing parameters, including fuel enrichment, fuel geometry, and cladding material, the study concludes that SiC is a feasible substitute for conventional zirconium cladding. The findings of this study have practical implications for the safety and efficiency of nuclear power plants. The use of SiC and FeCrAl cladding materials could significantly reduce the risks associated with loss-of-coolant accidents and other high-temperature conditions, thereby improving the safety and reliability of nuclear power plants.

Artificial Intelligence Driven Nuclear Reactor Instrumentation and Control Measurements In-core and Out-core in Nuclear Power Plants

The use of Artificial Intelligence (AI) in nuclear in-core instrumentation and control has the potential to improve the safety and efficiency of nuclear power plants. In-core instrumentation refers to the sensors and other measurement devices that are used to monitor the conditions inside the nuclear reactor core, such as temperature, pressure, and neutron flux. These measurements are used to control the operation of the reactor and ensure that it is operating within safe limits from Probabilistic Risk Assessment (PRA) point of view. One way that AI can be used in nuclear in-core and out-core Instrumentation and Control (I&C) as well as Instrumentation and Measurement (I&M) is by analyzing the data from these sensors in real-time and using machine learning algorithms to identify patterns and trends. This can help operators to detect potential problems or anomalies before they become critical, allowing them to take proactive measures to prevent accidents or malfunctions. AI can also be used to optimize the operation of the reactor by analyzing data from past operations and using this information to develop more efficient control strategies. For example, AI algorithms could be used to identify the most effective combination of control parameters to maintain a stable and safe reactor operation, while minimizing the use of fuel and other resources. Overall, the use of AI in nuclear In-core instrumentation and control has the potential to improve the safety, efficiency, and reliability of nuclear power plants. As AI technology continues to advance, we can expect to see more widespread adoption of these technologies in the nuclear industry

Mathematical modeling of temperature stresses of heat-generating elements of elliptical cross-section

The work is devoted to improving the reliability of nuclear power plants. During operation, the fuel elements of nuclear reactors operate at sufficiently high temperatures. The heterogeneity of the temperature field leads to the appearance of so-called thermal stresses. The main objective of this work is mathematical modeling of temperature stresses of fuel elements of cylindrical and elliptical cross-sections reactors. Based on mathematical modeling, it was shown that the level of thermal stresses arising during the transition from a circular cross-section to an elliptical one decreases.

Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

Innovative Development and Application of a Stress-Strength Model for Reliability Estimation of Aged LV Cables for Nuclear Power Plants

As a part of the H2020 EU Project called “TeaM Cables” - which has, among its aims, modelling reliability of nuclear power plant cables - the goal of this paper is to develop a model for the prediction of residual reliability of low voltage cables for nuclear power plants subjected to gamma radiation stress. The model estimates the probability that such cables withstand random stress overshoots in-service, which can occur, e.g., in case of nuclear accidents.

Electric Motors for Variable-Speed Drive of Lock Valves

Improving the operational reliability of nuclear power plants, combined heat and power plants (CHP), as well as oil and gas pipelines is a priority task in the development of a variable-speed drive for lock valves used at these facilities. This paper analyzes the technical requirements for such devices: the motor has been selected, its electrical equilibrium and moment equations have been obtained; recommendations for the selection of the kinematic drive scheme have been formulated. Based on the theoretical data obtained, a prototype has been developed, manufactured, and tested.

Probabilistic and deterministic investigation on single crack growth in dissimilar metal welds of a piping system

Probabilistic and deterministic integrity analyses of piping are important issues with regard to the integrity and reliability of nuclear power plant (NPP) components. Since there is an ongoing controversial discussion concerning the positive and negative influence of weld residual stress (WRS) on the failure probabilities of piping with dissimilar metal welds (DMWs), we herewith present the analysis of a representative straight pipe with DMW in a boiling water reactor (BWR). In this case, we consider the WRS profile, internal pressure (IP), bending stress (BS), stress corrosion cracking (SCC) and relative depth (a/t) of circumferential cracks at the inner pipe wall for the calculation of crack growth and break probabilities by using the PROST software. Probabilistic analyses show that the dependence of various break probabilities of components is not only on WRS distribution profiles but also on the crack geometry. The analyses reveal that the break probability may increase nearly 100 times due to an increase of the WRS by a factor of 1.5. The sensitivity of the stress intensity factors on the WRS profile, IP, BS, SCC and on the relative crack depth (a/t) is compared.

Lifetime estimation for optocouplers using accelerated degradation test

AbstractInstrumentation and control systems significantly affect the safety and reliability of nuclear power plants. In this study, we analyze the cause of failure of the electronic card constituting the instrumentation and control system as it is the most typical reason for the failure of optocouplers. The lifetime of optocouplers must be predicted accurately to ensure high‐reliability nuclear power plants. While acceleration tests have been performed to predict the lifetime of optocouplers more efficiently, it is preferable to perform accelerated degradation tests rather than accelerated life tests. In this paper, accelerated degradation tests are performed on optocouplers at various temperatures and voltages. To derive the degradation curve model of the optocoupler using the results of accelerated degradation tests, we use the most suitable model from OriginLab. The parameter values of the proposed degradation curve model are calculated by applying the Arrhenius model and Weibull distribution, and the lifetime of the optocoupler is estimated by using the proposed degradation curve model.

Partition method of wall friction and interfacial drag force model for horizontal two-phase flows

The improvement of thermal-hydraulic analysis techniques is essential to ensure the safety and reliability of nuclear power plants. The one-dimensional two-fluid model has been adopted in state-of-the-art thermal-hydraulic system codes. Current constitutive equations used in the system codes reach a mature level. Some exceptions are the partition method of wall friction in the momentum equation of the two-fluid model and the interfacial drag force model for a horizontal two-phase flow. This study is focused on deriving the partition method of wall friction in the momentum equation of the two-fluid model and modeling the interfacial drag force model for a horizontal bubbly flow. The one-dimensional momentum equation in the two-fluid model is derived from the local momentum equation. The derived one-dimensional momentum equation demonstrates that total wall friction should be apportioned to gas and liquid phases based on the phasic volume fraction, which is the same as that used in the SPACE code. The constitutive equations for the interfacial drag force are also identified. Based on the assessments, the Rassame-Hibiki correlation, Hibiki-Ishii correlation, Ishii-Zuber correlation, and Rassame-Hibiki correlation are recommended for computing the distribution parameter, interfacial area concentration, drag coefficient, and relative velocity covariance of a horizontal bubbly flow, respectively.

Applicability of non-destructive laser ultrasound and non-linear ultrasonic technique for evaluation of thermally aged CF8 duplex stainless steel

Duplex stainless steel is a structural material that is highly used in nuclear power plants, especially in the reactor core components and pipelines. Long-term exposure of duplex stainless steel in a high-temperature working environment will cause thermal aging embrittlement. Material deterioration will affect the long-term reliability of nuclear power plants. In this work, we used two different ultrasonic techniques, namely Laser Ultrasound Technique (LUT) and Non-linear Ultrasonic Technique (NUT) for measuring the material properties. The LUT has unique advantages such of being non-contact, non-destructive, and remotely operable, while NUT has considerable measurement sensitivity to the microstructure changes of materials. Therefore, this research mainly focused on duplex stainless steel after the heat aging treatment. The changes in the material properties of the duplex stainless steel after the heat aging treatment were investigated by ultrasonic testing and traditional material testing methods. The results of this study show that the thermal aging with higher duration, the ferrite phase decomposes into Cr-rich αˊ-phase and Fe-rich α-phase. The hardness of both the austenite iron phase and the ferrite iron phase increase with the increase in heat aging time. The phase velocity of duplex stainless steel increases significantly up to a heat aging of 2400 h, and then the phase velocity decreases significantly. After thermal aging treatment at different times, the non-linear factor did not change significantly due to the hardness changes. The non-linear response caused by the heat aging treatment of duplex stainless steel is minimum.

Research on the Reliability of the Operating Performance of the Relief Valve of Nuclear Power Plant under Seismic Conditions

Nowadays, environmental and energy problems are becoming increasingly prominent. Nuclear power plants are an important means to solve these two problems. Therefore, research on the reliability of nuclear power plants is closely related to environmental and energy issues. The reliability research of nuclear safety valves has always been the top priority in the reliability research of nuclear power plants, so the reliability of the operation performance of such relief valves must be considered under seismic conditions. This article borrows ANSYS to carry out fluid-solid coupling simulation of relief valves, including simulations under seismic conditions and non-seismic conditions, and explores changes during opening and closing under various conditions, including the opening height, pressure and velocity in internal flow field ,inlet pressure ,solid stress and solid strain. Finally, a test comparison shows that the effect of longitudinal seismic load on the return pressure of the relief valve can be ignored.

Quantitative software reliability assessment methodology based on Bayesian belief networks and statistical testing for safety-critical software

This study proposes an overall methodology that provides in-depth evidence on software reliability. It is used to quantitatively assess the reliability of nuclear power plant (NPP) safety-critical software for the incorporation of digital instrumentation and control systems into NPP probabilistic risk assessment (PRA). The methodology consists of three parts: (1) the relationships among the software development life cycle (SDLC) phases, the number of remaining faults in the software, and the probability of failure on demand (PFD) are modeled by a Bayesian belief network, which can provide a prior distribution of the software PFD; (2) a reliability model for the PFD is used to calculate the number of no-failure tests needed to meet the expected reliability target according to the prior distribution; (3) the software statistical testing (SST) based on PRA is used as a reliability validation test method to assess reliability, when the required no-failure tests are completed, it is considered that the software meets the expected reliability target. The main contribution of this methodology is that it fully considers the factors that affect software reliability, i.e. the quality of development activities and verification & validation (V&V) activities of the SDLC processes, software operational profile and software operational environment when assessing software reliability. This is done such that the methodology overcomes the subjectivity of separate quality assessments of the SDLC processes. It also solves the problem that occurs because an individual SST using an uninformative prior distribution is conservative.

Numerical optimization of a finned tube bundle heat exchanger arrangement for passive spent fuel pool cooling to ambient air

The passive cooling of nuclear spent fuel pools is a promising alternative to active cooling. Since such systems work even in safety-critical situations, e.g. station blackout, the reliability of nuclear power plants would be enhanced. As in such systems heat needs to be transfer to the environment, the heat exchanger to air has a crucial influence on the system performance. This paper describes investigations of the Nusselt number, the achievable efficiency and the volumetric heat transfer coefficient of the tube bundle heat exchangers for a passive cooling system located at the bottom of a chimney. The effect of tube bundle configuration, tube shape, longitudinal tube pitch, transversal tube pitch and tube row number on natural convection heat transfer was numerically studied. These parameters were varied to optimize the heat transfer performance of the heat exchanger. It was found, that the staggered configuration performs better than the inline arrangement, since the flow mixing is higher. Furthermore circular tube shape and an oval tube shape with the aspect ratio of 1:2.1 were optimum for the inline and staggered configuration respectively. The longitudinal and transversal tube pitches of 63mm and 65mm performed best, since higher values reduced heat transfer. A tube row number greater than 5 did not improve the heat transfer and therefore a tube row number of 5 is recommended. The Nusselt number and volumetric heat transfer coefficient of the optimized tube bundle arrangement enhanced by 15.4% and 47.8% respectively at a temperature difference of 40K compared to the initial design.

Методика определения времени безопасной эксплуатации оборудования и трубопроводов на основе результатов неразрушающего контроля

The reliability of nuclear power plants (NPPs) has an influence on power generation safety and stability. The reliability of NPP equipment and pipelines (E&P), and the frequency of in-service inspections are directly linked with damage mechanisms and their development rates. Flow accelerated corrosion (FAC) is one of significant factors causing damages to E&P because these components experience the influence of high pressure, temperature, and high flow velocity of the inner medium. The majority of feed and steam path components made of pearlitic steels are prone to this kind of wear. The tube elements used in the coils of high pressure heaters (HPH) operating in the secondary coolant circuit of nuclear power plants equipped with a VVER-1000 reactor plant were taken as the subject of the study. The time dependences of changes in the wall thickness in HPH tube elements are studied proceeding from an analysis of statistical data of in-service nondestructive tests. A method for determining the initial state of the E&P metal wall thickness before the commencement of operation is proposed. The article presents a procedure for predicting the distribution of examined objects' wall thicknesses at different times of operation with determining the occurrence probability of damages caused by flow accelerated corrosion to calculate the time of safe operation until reaching a critical state. A function that determines the boundary of permissible values of the HPH wall thickness distributions is obtained, and it is shown that the intervals of in-service inspections can be increased from 6 years (the actual frequency of inspections) to 9 years, and the next in-service inspection is recommended to be carried out after 7.5 years of operation. A method for determining the existence of FAC-induced local thinning in the examined object has been developed. The developed approaches and obtained study results can be adapted for any pipelines prone to wall thinning to determine the frequency of in-service inspections (including an express analysis based on the results of a single nondestructive in-service test), the safe operation time, and quantitative assessment of the critical value reaching probability.

Effect of the Surface Scratch Angle on the Mechanical Characteristics of Nickel-Based Alloy

Stress corrosion cracking (SCC) produced by surface scratches has great effect on the safety and reliability of nuclear power plant. To understand the effects of the angle of surface scratches on SCC behavior of nickel-based alloy, the stress and strain field nearby the surface scratches of nickel based alloy is simulated using ABAQUS. Results show that the high stress and strain region increases as the angle increases, which leads to an increase in SCC growth rate. The high stress and strain region offset toward the scratch angle, which indicates that the crack is more likely to crack and extend along the direction of scratch angle.

Kernel approaches for fault detection and classification in PARR-2

Safety and reliability of nuclear power plants is of utmost importance. For that purpose, modern fault detection and classification (FDC) techniques are being devised to compliment the existing hardware redundancy and limit checking techniques. Among these modern techniques, Fisher discriminant analysis (FDA) and support vector machines (SVM) have been shown to be successful for FDC of nuclear reactors. By considering the fact that both FDA and SVM are basically established for linear systems and nuclear reactors are highly nonlinear processes, it becomes more intuitive to utilize some nonlinear FDC technique. To this end, application of kernel based non-linear approaches including kernel FDA (KFDA) and kernel SVM (KSVM) is proposed in this paper for fault detection and classification in Pakistan Research Reactor-2. Control rod withdrawal and accidental external reactivity insertion faults are manually executed at PARR-2, and training data is collected from the reactor based on which KFDA and KSVM models are developed. The online data is subsequently tested using the developed models which resulted into reliable fault classification.