Operation Of Nuclear Power Plants Research Articles

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.

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

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

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.

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 circuits. 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 failure 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.

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.

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

HK-SPSA based performance optimization method for steam generator liquid level control

Steam generator level control systems play a crucial role in ensuring safe, economical, and stable operation of nuclear power plants. However, traditional control systems exhibit limited efficiency in commissioning and are susceptible to human error. Additionally, traditional parameter tuning methods are typically experienced-based, cumbersome, and time-consuming, making it challenging to obtain optimal parameters. To address these issues, we propose a hybrid knowledge-guided improved simultaneous perturbation stochastic approximation algorithm (HK-SPSA) to optimize the control parameters of steam generator level control systems. Firstly, the algorithm utilizes iteration point adjacency information to approximate the current optimization process status and guide the adaptive adjustment of the iteration step size. Secondly, it utilizes composite gradient information generated by the estimated historical and current gradients to guide the optimization direction and the tuning of iteration step size. Simulation experiments have shown that HK-SPSA improves optimization performance and reduces the costs of optimization and adjustment compared to traditional methods.

Prolongation of Safe Operation of the K-1000-60/3000 Turbine Power Unit after Damage to the HPC Rotor

Currently, when large-scale military actions are taking place on the territory of Ukraine, the inclusion of the domestic energy system in the European one is a reliable component of providing electricity to the country's energy market. However, according to experts, uninterrupted and safe operation of nuclear power plants is still considered a prerequisite for the stable operation of the energy sector of Ukraine. The purpose of the paper is to assess the damageability and individual resource of the rotor of high-pressure cylinder (HPC) of the K-1000-60/3000 turbine power unit of the LMZ after damage to the blades in order to prolong the operation of the power unit in the conditions of a stressed state of the power system. One of the most effective ways to partially solve the problem of replacement of generating capacities is to extend the operating periods of NPP power units after the end of the project operating period, provided that nuclear and radiation safety standards are met. The review of the previously established service life of the energy equipment of NPP power units involves the assessment of the residual resource of the energy equipment in accordance with the regulatory documents. After the accidental damage of the blades of the last stage of the HPC rotor of the K-1000-60/3000 turbine power unit of the LMZ, there was a need to study the cyclic and static damage, the individual residual resource of the HPC rotor. In the process of achieving the goal, studies were carried out for three design options: the original option (five stages of the HPC rotor), the option without the blades of the last stage and the option without the fifth stage (with four first stages). The calculation of the resource indicators of the rotor in the execution of the HPC without blades of the 5th stage shows that the static damage accumulated in the main metal is 52%, the cyclic damage is 5% when applying the standard strength reserves for the number of cycles and for deformations at the level of nN=10 and nε=1.5 according to the recommendations of SOU-N MEV 40.1-21677681-52:2011. Thus, the total damage to the base metal is 57%, which sets the residual resource of the HPC rotor at the level of 88.4 thousand hours. The calculation of the resource indicators of the rotor in the execution of the HPC without entire 5th stage shows that the static damage accumulated in the base metal is 52%, the cyclic damage is 6% when applying the standard strength reserves for the number of cycles and deformations at the above-mentioned level. The total damage to the base metal is 58%, which determines the residual resource of the HPC rotor at the level of 85.6 thousand hours.

Methods of determining internal damage in power transformers

Due to the variety of defects that arise in electrical machines, it is necessary to use more effective methods for monitoring their condition. All over the world research and development of new means and methods for monitoring powerful electrical machines during their operation is underway. Some examples of recent advances are vibration diagnostic methods for assessing the compaction of components inside a transformer, acoustic and electrical systems for monitoring partial discharges, data processing using digital methods, and new sensors for continuous monitoring of gases and moisture in oil, as well as hot spot temperatures. Additionally, thermal imaging testing of power equipment is also an important tool to ensure reliable operation. It is believed that the most effective method is gas chromatographic oil analysis, which can identify most defects in oil-filled equipment. During the operation of powerful electrical machines, the use of existing non-destructive testing methods does not allow a complete assessment of the condition of the main parts of the equipment, as an analysis of damage locations shows, 25% are damage to the core and windings. Effective monitoring of the condition and determination of the performance of transformers is of particular importance, since they are key elements in the operation of nuclear power plants. The paper considers the possibility of using the eddy current testing method; determining a defect in magnetic core steel is based on fixing the unevenness of the magnetic field on the horizontal or vertical planes of a yoke or rod consisting of electrical steel plates. Monitoring and evaluating the functioning of existing equipment, detecting deficiencies in the early stages of their development, when repair costs are still minimal, and preventing emergency failures become a priority. In accordance with the growth rate of detected deficiencies, monitoring is carried out from time to time or continuously, the maximum number of monitored characteristics is achieved when the transformer is fully tested to determine its functionality.

Evaporator with horizontal water plane stabilization

Evaporation plays an important role in maintaining water balance and managing water resources in reservoirs. This problem is relevant in hot regions with a lack of fresh water. In addition, reliable operation of evaporators plays an important role in preventing overheating and ensuring safe cooling of reactors which is critical to maintaining nuclear safety. The complete control and accurate measurement of the evaporation process provided by the developed device enables efficient water management ensuring the stability of nuclear power plants and minimising environmental risks. Evaporators are used to control the evaporation process in reservoirs. Based on the analysis of existing evaporators and their shortcomings, an evaporator with horizontal plane stabilization in the pan is developed. Due to this function the developed device allows to control the evaporation process with higher accuracy by reducing fluctuations in the water level in the evaporator. The developed device contains an autonomous power supply that uses energy-saving technology based on a solar battery which ensures operation without additional sources. The use of such a power supply increases the evaporator reliability. One of the important advantages of this device is the use of a cellular-based information transmission channel to the control center which allows to transmit the results of evaporation quickly monitoring and make water management decisions accordingly. In addition, the use of a GPS navigation system allows to set or determine the coordinates of the evaporator installation in the reservoir accurately. The developed device can be included in an intelligent energy resource management system; its use will significantly improve the safety and sustainability of nuclear power plants by improving evaporation process control. It will also optimize the nuclear power plants operation, promote efficient management of water resources and ensure safety in the nuclear energy field.

Safety evaluation of Multiple Steam Generator Tube rupture accident using the best estimate plus uncertainty approach

Following the disaster in Fukushima Nuclear Power Plant (NPP) in 2011, the awareness of securing the safety of NPP under extreme events has been raised. For this purpose, the concept of Design Extension Conditions (DECs) was introduced, to enhance the plant’s capability to withstand events that are more severe than Design Basis Accidents (DBA), such as Multiple Steam Generator Tube Rupture (MSGTR) accident, given its rapid progression and significant potential for large release of radioactive materials to the environment. It is worth noting that MSGTR has never occurred during the commercial operation of nuclear power plant, therefore the knowledge of consequences of the event and potential mitigation strategy is limited. In this study, a postulated MSGTR (simultaneous rupture of 5 U-tubes in a single SG) accident is analysed using the best-estimate thermal hydraulics code RELAP5/SCDAPSIM/MOD3.4. The main focus of this work is evaluation of appropriate operator mitigation actions under various inherent uncertainties. To this end, the best estimate plus uncertainty (BEPU) approach is adopted, whereby RELAP5/SCDAPSIM/MOD3.4 was coupled to the statistical analysis software, DAKOTA, to conduct the uncertainty quantification (UQ). Using BEPU analysis, an ensemble of system responses under various epistemic and aleatory uncertainties is obtained and used to validate the efficacy of operator actions in bringing the plant to a safe shutdown condition.

An Association Rule Mining-Based Method for Revealing the Impact of Operational Sequence on Nuclear Power Plants Operating

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators.

Dynamics optimization of small branch pipes in nuclear power plants based on machine learning algorithms

Vibration fatigue failure of small branch pipes poses a great threat to the safe operation of nuclear power plants. However, the transient and wide-band vibration problems are not adequately considered in ASME and RCC-M code, resulting in repeated fatigue failures. In addition, the current research mainly focuses on vibration test methods and fatigue analysis methods, neglecting the study of pipeline vibration characteristics. Therefore, innovative approaches were essential for effectively managing complex dynamic loads. In this study, an innovative approach combining backpropagation artificial neural networks (BP-ANN) and non-dominated sorting Genetic Algorithm II (NSGA-II) was proposed to optimize the vibration of these pipes. The goal was mitigating vibration-induced failures by enhancing operational stability. The methodology progressed through several key stages. Firstly, BP-ANN was utilized for regression analysis, correlating pipe characteristics to vibration effects. Through regression analysis, the complex interrelationships governing the pipes' dynamic behavior was revealed. Subsequently, based on the regression model, NSGA-II was used to derive an optimal combination of design parameters to minimize the vibration response. The proposed technique was validated on an L-shaped cantilevered pipe via finite element simulations and physical experiments. The analysis case shows that the BP-ANN model demonstrated excellent accuracy in predicting vibration responses. Meanwhile, NSGA-II successfully revealed the trade-offs between conflicting objectives, generating a Pareto-optimal set balancing stability under different excitation directions. This study highlights the potential of machine learning methods for dynamic optimization of small branch pipes in nuclear power plants, and verifies the accuracy and effectiveness of the method through experiments. The research results provide a new idea for small branch pipe design and vibration control of nuclear power plant, which contributes to enhancing the safety and reliability of small branch pipes.

Mobile Robot for Security Applications in Remotely Operated Advanced Reactors

This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for physical security, aiming to replace labor-intensive methods. Focusing on the human–robot interaction principle, the review conducts a state-of-the-art analysis of dog robots’ potential in infrastructure security and remote inspection within human–robot shared environments. Additionally, this paper surveys research on the capabilities of mobile robots, exploring their applications in various industries, including disaster response, exploration, surveillance, and environmental conservation. This study emphasizes the crucial role of autonomous mobility and manipulation in robots for diverse tasks, and discusses the formalization of problems, performance assessment criteria, and operational capabilities. It provides a comprehensive comparison of three prominent robotic platforms (SPOT, Ghost Robotics, and ANYmal Robotics) across various parameters, shedding light on their suitability for different applications. This review culminates in a research roadmap, delineating experiments and parameters for assessing dog robots’ performance in safeguarding nuclear power plants, offering a structured approach for future research endeavors.