Nuclear Power Plant Components Research Articles

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.

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

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

Estimation of residual stress in dissimilar metals welding using deep fuzzy neural networks with rule-dropout

Welding processes are used to connect several components in nuclear power plants. These welding processes can induce residual stress in welding joints, which has been identified as a significant factor in primary water stress corrosion cracking. Consequently, the assessment of welding residual stress plays a crucial role in determining the structural integrity of welded joints. In this study, a deep fuzzy neural networks (DFNN) with a rule-dropout method, which is an artificial intelligence (AI) method, was used to predict the residual stress of dissimilar metal welding. ABAQUS, a finite element analysis program, was used as the data collection tool to develop the AI model, and 6300 data instances were collected under 150 analysis conditions. A rule-dropout method and genetic algorithm were used to optimize the estimation performance of the DFNN model. DFNN with the rule-dropout model was compared to a deep neural network method, known as a general deep learning method, to evaluate the estimation performance of DFNN. In addition, a fuzzy neural network method and a cascaded support vector regression method conducted in previous studies were compared. Consequently, the estimation performance of the DFNN with the rule-dropout model was better than those of the comparison methods. The welding residual stress estimation results of this study are expected to contribute to the evaluation of the structural integrity of welded joints.

Ultrasonic wave propagation analyses in centrifugally cast stainless steel using solidification grain structure models

Several components of nuclear power plants are made from cast austenitic stainless steel (CASS) because of its high corrosion resistance and strength. CASS that is centrifugally fabricated in a rotating mold is used in primary coolant piping. In-service inspection based on ultrasonic testing (UT) has to be conducted for primary coolant piping in accordance with fitness-for-service codes. However, a high-accuracy evaluation of flaws in CASS components through UT is difficult because the ultrasonic waves are scattered and attenuated by the coarse grains, and the ultrasonic beam is distorted by the anisotropy resulting from the grain orientations. Some works have attempted to improve UT capability for CASS components. For more improvement, it is essential to understand how ultrasonic waves propagate in the solidification structure of CASS. Numerical simulations are a useful and reasonable way for this purpose. The simulation model should account for a three-dimensional grain structure. In this study, we prepared three different models of solidification structures for centrifugally CASS, and we fed these structures into an explicit finite element model to simulate the propagation of ultrasonic waves. Afterward, we compared the simulated wave propagations with those measured by a laser Doppler vibrometer to determine an appropriate model that showed good agreement between the simulated and experimental results.

Development and validation of a new one-dimensional parallel transient system solver with coordinate transformation method developed in OpenFOAM

Passive containment cooling system is an important component of both 3rd and 4th generation nuclear power plants. To simulate two-phase flow in loop-type passive containment cooling system, a new one-dimensional parallel transient system solver, ComspaFOAM-SYS1D, was developed based on OpenFOAM. Compared to the previously developed one-dimensional solver, the new solver can be accelerated using multithreading. Additionally, a new coordinate transformation method was used in ComspaFOAM-SYS1D, enabling OpenFOAM to simulate a one-dimensional loop with bends. Faucet problem was used to verify the basic equations and models in the new solver. Validation was undertaken based on the GENEVA facility and the passive containment cooling system comprehensive performance test facility. In the assessments, representative parameters such as void fraction, pressure, temperature, phase velocity, and oscillation period were compared. Regarding efficiency, the simulation results indicated that the parallel speedup of the new solver could approach a value of approximately 2.0. In terms of accuracy, the relative deviations of time-averaged and transient extremum values of all the simulation results from the experimental results were within 15.0%.

Fault diagnosis of nuclear power plant sliding bearing-rotor systems using deep convolutional generative adversarial networks

Sliding bearings are crucial rotating mechanical components in nuclear power plants, and their failures can result in severe economic losses and human casualties. Deep learning provides a new approach to bearing fault diagnosis, but there is currently a lack of a universal fault diagnosis model for studying bearing-rotor systems under various operating conditions, speeds and faults. Research on bearing-rotor systems supported by sliding bearings is limited, leading to insufficient fault data. To address these issues, this paper proposes a fault diagnosis model framework for bearing-rotor systems based on a deep convolutional generative adversarial network (TF-DLGAN). This model not only exhibits outstanding fault diagnosis performance but also addresses the issue of insufficient fault data. An experimental platform is constructed to conduct fault experiments under various operating conditions, speeds and faults, establishing a dataset for sliding bearing-rotor system faults. Finally, the model's effectiveness is validated using this dataset.

Evaluation of Worker Radiation Exposure during the Kori Unit 1 Steam Generator Dismantling Process

Kori Unit 1 was permanently shut down on June 18, 2017. Since then, Korea is actively preparing for the decommissioning of the nuclear power plant. Because decommissioning work is performed in a radioactive environment, worker radiation exposure is a significant consideration. In this study, worker radiation exposure is evaluated during the steam generator, one of the heavy components of nuclear power plant, dismantling process. A radiation evaluation for the dismantling process is performed using the code RESRAD-BUILD. A steam generator dismantling scenario and optimal cutting method are designed to evaluate worker radiation exposure, considering pipe dimensions, cutting tool speed, and experience in steam generator replacement. The evaluation results are derived for each work type and year. As a result of the evaluation, worker radiation exposure is 7.5 man-mSv at the year of planned decommissioning.

Development of a Diffusion Bonding Route for Joining Oxide-Dispersion-Strengthened (ODS) Steels for Nuclear Applications

Oxide-dispersion-strengthened (ODS) steels are candidate materials for components in current and future nuclear power plants. One of the issues with using ODS steels is the difficulty of joining them without loss of mechanical performance. In this study, austenitic ODS 316L stainless steel was diffusion-bonded to Inconel 718 superalloy. Having optimized the bonding conditions, a number of samples were made at 1200 °C with a bonding time of 1 hour and pressure of 10 MPa. Preliminary mechanical and microstructural analyses indicated the formation of a sound joint interface, despite slight grain growth in the ODS 316L. A post-bonding thermo-mechanical treatment (TMT) was conducted to refine and restore the microstructure of the ODS 316L. Comparative TEM investigations of the parent ODS alloy and the bonded samples (with and without TMT) along with statistical analyses showed that the Y–Ti–O oxide size distribution remains unaffected by the bonding and complementary TMT cycles, indicating stability of such particles even at very high temperatures and suitability of the devised route for joining the ODS 316L steel.

Non-equibiaxial residual stress evaluation methodology using simulated indentation behavior and machine learning

Measuring the residual stress in the components in nuclear power plants is crucial to their safety evaluation. The instrumented indentation technique is a minimally invasive approach that can be conveniently used to determine the residual stress in structural materials in service. Because the indentation behavior of a structure with residual stresses is closely related to the elastic-plastic behavior of the indented material, an accurate understanding of the elastic-plastic behavior of the material is essential for evaluation of the residual stresses in the structures. However, due to the analytical problems associated with solving the elastic-plastic behavior, empirical equations with limited applicability have been used. In the present study, the impact of the non-equibiaxial residual stress state on indentation behavior was investigated using finite element analysis. In addition, a new nonequibiaxial residual-stress prediction methodology is proposed using a convolutional neural network, and the performance was validated. A more accurate residual-stress measurement will be possible by applying the proposed residual-stress prediction methodology in the future.

Uncertainty and statistics of dislocation-oxide interactions on strength and creep failure of oxide dispersion strengthened steels

Oxide dispersion strengthening (ODS) steels exhibit excellent mechanical and creep properties due to the nanoscale oxide particles as barriers to dislocation motion, for the great application potentials to structural components of advanced nuclear power plants. However, the classical ODS model described by the Orowan mechanism or cutting mechanism only depends upon a single average size, ignoring uncertainty and statistics of dislocation-oxide interactions induced by the spatial and dimensional random distribution of oxide particles. When the oxide particle size is larger than the critical size to determine the interaction mechanism transformation from cutting to looping, the Orowan mechanism and cutting mechanism would exist simultaneously. Here we propose a modified ODS model for strength and creep rate considering the spatial distribution and size distribution of oxide particles, which is more consistent with the experimental result compared to the classical ODS model. In addition, the contribution of oxide particles to the yield strength and creep strength is evaluated quantitatively for various precipitate sizes and volume fractions. It is believed that this model provides an analytical framework to accurately predict the strength and creep rate of ODS steels.

A fault diagnosis method for nuclear power plant rotating machinery based on adaptive deep feature extraction and multiple support vector machines

Rotating machinery is the essential component in nuclear power plants (NPPs). Effective fault detection and diagnosis is a main challenge in the operation and maintenance of NPPs rotating machinery. The performance of traditional fault diagnosis methods mainly depends on complex manual feature extraction and sufficient expert prior knowledge. This study proposes a fault diagnosis method based on adaptive feature extraction and multiple support vector machines (ResNet-SVMs) to overcome the limitations of the traditional intelligent fault diagnostics. First, the vibration information from different locations of the rotating machinery is collected and used as input data for the algorithm model. Then, the deep residual neural network adaptively extracts fault features from input data to obtain feature data of different depths. Finally, multiple support vector machines identify the feature data to realize the fault diagnosis. The effectiveness verification is carried out based on the experimental cases of induction motor and rolling bearing fault diagnosis. Compared with other advanced intelligent fault diagnosis methods, ResNet-SVMs model provides better diagnostic performance, which demonstrates its potential value for NPPs rotating machinery fault diagnosis.

Verification strategy for artificial intelligence components in nuclear plant instrumentation and control systems

The application of artificial intelligence (AI) systems in nuclear power plants has the potential to facilitate significant advancements in the industry. However, nuclear system engineers and regulators have expressed concerns regarding the reliability of AI components owing to a lack of documentation and appropriate verification methods that differ from those used for traditional software. Accordingly, this study proposes a systematic approach for the verification and validation of AI components based on nuclear software regulatory guidelines. The proposed strategy includes process verification and functional testing activities specifically tailored to AI components for instrumentation and control systems. This approach serves as a foundation for fostering understanding and agreement among system engineers, operating licensees, and regulatory bodies on the use of AI components in nuclear power plants.

Role of Fe in long-range ordered Ni2Cr precipitates in Ni-Cr-Fe model alloys during isothermal aging

The precipitation of new phases during long-term service at elevated temperatures is a concern for the thermal stability of engineering alloys. In Ni-Cr-based alloys, e.g., Alloy 690, the formation of long-range ordered Ni2Cr causes embrittlement and may impact the lifetime of nuclear power plant components. In this work, we quantify the formation and evolution of Ni2Cr precipitation in eleven Ni-Cr-Fe model alloys with 0, 5, 7, and 10 wt % Fe contents, and with Ni/Cr atomic ratios of 1.8, 2.0, 2.2, 2.4. These alloys were isothermally aged up to 10,000 h at temperatures between 330 and 475 °C. The alloys were characterized by synchrotron-based x-ray diffraction and Vickers hardness testing to quantify Ni2Cr precipitate size, and the impact of precipitate size on the mechanical properties as a function of Fe content. After 10,000 h of aging at 475 °C and 418 °C, the formation of Ni2Cr was observed in all alloys with 0 and 5 wt % Fe. After 10,000 h of aging at 418 °C, Ni2Cr precipitates were also observed in the 7 wt % Fe containing Ni/Cr=2.0 sample. No clear evidence of Ni2Cr was observed in any of the 10 wt % Fe samples at any time and temperature combination. We find that the face-centered cubic matrix lattice contraction and Vickers hardness are correlated with the Ni2Cr formation. The greatest change in hardness and lattice contraction occurs in stoichiometric alloys (Ni/Cr=2.0) with 0 wt % Fe at 475 °C. The rate of change in the material properties for the 5 wt % Fe alloys is reduced, however the magnitude of changes is similar to 0 wt % Fe alloys. A precipitation hardening model developed for Ni-Cr alloys based on critical resolved shear stress with weakly coupled dislocations shows a clear link between Ni2Cr precipitate size and hardness. This trend held across all alloys with Ni2Cr formation regardless of Fe concentration. This important structure-property relationship can potentially help define Ni-Cr-Fe-based component lifetimes directly through an understanding of how Ni2Cr formation impacts mechanical properties as a function of Fe content.

Age-hardening behaviors of the weld metals of 22% Cr and 25% Cr duplex stainless steels at 400 °C

Duplex stainless steel (DSS) alloys are attractive materials for pressure components in nuclear power plants as they offer a good combination of high strength, corrosion resistance, and weldability. However, DSS suffers from embrittlement and hardening upon exposure to temperatures of 250–500 °C. Most previous studies addressed only solution-treated materials, and limited research has been conducted on weld metals. In this study, the relationship between the nanostructural evolution and hardening of 22% Cr and 25% Cr DSS weld metals (fabricated with grade-2209 and 2594 welding materials, respectively) during aging at 400 °C was investigated. The results revealed that the degree of hardening of ferrite after aging at 400 °C for 1000 h was greater in the 22% Cr DSS weld metal than in the 25% Cr DSS weld metal. The ferrite phases of both samples underwent two different phase decomposition processes during aging, (1) phase separation (α → Fe-rich α + Cr-rich α’) and (2) solute clustering to form G-phase precursors, both of which were identified within the body-centered cubic structure of ferrite in the weld metals. Phase separation was less pronounced, whereas formation of G-phase precursors was more pronounced in the 22% Cr DSS weld metal than in the 25% Cr DSS. These results indicate that the extent of formation of G-phase precursors has a significant influence on predicting the degree of hardening of ferrite. The enhanced formation of G-phase precursors in the 22% Cr DSS weld metal was elucidated using thermodynamic simulations; the formation of the G-phase precursor was predominantly facilitated by the higher Mn fraction in the 22% Cr DSS weld metal. The findings of the present study are expected to aid the selection and/or further improvement of the compositional design of DSS welding materials, thereby increasing the long-term safety of DSS welded components in nuclear applications.

Structural, physical, and radiation absorption properties of a significant nuclear power plant component: A comparison between REX-734 and 316L SS austenitic stainless steels

Abstract Austenitic stainless steels (SSs) are commonly used as in-core and surrounding structural materials in today’s industrial BWR and PWR systems. Such adaptable steels have also been the primary materials studied and used in several advanced nuclear reactor technologies, such as fast breeding and magnetic fusion reactors. In this study, some critical material properties, such as structural, physical, and radiation-shielding properties of REX-734 and 316L SS, were experimentally evaluated and compared to those of a number of other alloys. In addition to homogeneous element distribution, both alloys exhibit strong crystal orientation. The REX-734 alloy has a tensile strength of 1,259 MPa, whereas the 316L SS alloy has a tensile strength of 495 MPa. Moreover, nitrogen in the REX-734 alloy formed ultra-hard nitrides with Cr, Nb, and Si and precipitated into the structure and increased the strength. According to our findings, the mass attenuation coefficient values of the 316L SS sample were slightly higher than those of the REX-734 sample at all energies. It can be concluded that the REX-734 sample, with its exceptional strength qualities and excellent radiation attenuation capabilities, may be a viable nuclear power plant material for future investigations.

Investigation of microstructure, mechanical, and corrosion properties of Inconel 617 joints welded by laser–MIG hybrid welding

The demand for Nickel-based superalloy of Inconel 617 material is increasing for manufacturing hot section components of gas turbines, chemical, and nuclear power plants. It has excellent corrosion resistance in high-temperature environments. In the present study, laser–MIG hybrid welding consisting of a 3.5 kW CO2 laser in combination with a MIG welding system was utilized to weld 10 mm thick Inconel 617 plates with the wire feed rates (WFR) of 10 and 11 m/min in a single pass to analyze the welds microstructure, mechanical, and corrosion properties. The results revealed that the microstructure of the weld zone is composed of cellular crystals with dendrites and equiaxed sub-grains. Electron backscattered diffraction (EBSD) studies revealed coarse columnar dendrites and equiaxed grains at the center of weld joints with the same crystallographic orientation across the fusion boundary. The XRD analysis revealed the dominant gamma and gamma prime (γ/γ′) [(Ni/Ni3(AlTi))] phases along with the presence of minor carbide peaks such as Ti (C, N), M23C6, Cr (C, N), and M6C. The BM has a lower hardness (232 HV) and higher impact energy (236 J) than the weld joints. Similarly, the increase in WFR results in a decrease in microhardness (268–256 HV) values and an increase in impact energy (199–212 J). The weld joints exhibit good bend ductility without any cracks. The potentiodynamic polarization results revealed a lower corrosion rate of 0.212 mpy for the base metal than for the weld joints. The increase of WFR increased the weld metal corrosion rate from 0.342 to 0.427 mpy due to an increase in heat input (0.516–0.615 kJ/mm) and solidification time, which caused the coarsening of the grains resulting in lower grain boundary density and anode reaction to accelerate, which deteriorated the corrosion resistance of the welds.

A fault diagnosis of nuclear power plant rotating machinery based on multi-sensor and deep residual neural network

Rotating machinery is a key component of nuclear power plants (NPPs). The integrity of rotating machine is related to the safety and economy of the entire NPPs. In order to achieve better and more robust diagnostic performance, this work proposes an intelligent fault diagnosis method based on multi-sensor and deep residual neural network. This method gives full play to the value of multi-sensor information, and utilizes the powerful learning ability of deep learning model to realize the identification of rotating machinery fault types. The effectiveness of the method is evaluated by using the motor dataset and the bearing dataset from fault simulation experiment bench. In addition, the anti-noise ability of the method is tested and compared with other methods. The results show that the proposed method has higher diagnosis accuracy and stronger robustness, demonstrating the potential application value in rotating machinery of NPPs.

A Numerical Study on the Characteristics of the Pressurized Water Reactor’s (PWR) Primary Moisture Separator Using the Particle Tracking Method

The primary moisture separator—a key component in the PWR nuclear power plant—determines the quality of supplied steam to a turbine. Investigating its characteristics is important because supplying steam with excessive droplet entrainment results in damages to pipes, valves, and turbines in power plant circuits. In this numerical study, the particle tracking method in the Eulerian–Lagrangian methodology is used to investigate the characteristics of a primary moisture separator. Various swirl vanes with different bending angles, vane quantities, and vane locations are chosen to investigate the effect of design parameters on characteristics of the primary moisture separator. Additionally, the water droplet size is considered to vary from 0.01 to 50 μm in this study. The pressure drop between the inlet and outlet, the steam quality at the orifice outlet, and the particle collection ratio are discussed in this paper. The results show that steam quality increases as the bending angle decreases, and increasing the number of swirl vanes increases both the pressure drop and the steam quality.

Methodology for lifetime characteristics assessment of mechanical equipment in nuclear power plants. Ageing management of NPP mechanical equipment

In the 20s of the twenty-first century many nuclear power plants around the world have reached the final years of their design lifetime. The technical aspects of Plant Life Extension (PLEX) activities involve tests, in addition to the routinely performed ones, of the materials and structures of reactor units, as well as the carrying out operability analyses. In relation to this, the absence of regulatory and methodological basis becomes apparent, in Bulgaria as well. In recent years, new approaches have been adopted for evaluation of lifetime characteristics of components. The algorithm implemented is based on the active mechanism of degradation of the mechanical properties of materials. This paper discusses PLEX stages and corresponding activities. A methodology has been developed to assess the lifetime characteristics of Nuclear Power Plant (NPP) components such as to suit the stages of plant life extension and long-term operation. The methodology consists of: 1) Equipment classification; 2) Identification of the major degradation mechanisms of the mechanical properties of materials; 3) Determining the degradation effects of the mechanical properties of materials; 4) Identification of methods to control degradation effects (ageing); 5) Development of methodologies for evaluation of the ageing effects.

Development and Calibration of Thermoelectric-Power-Based Non Destructive Evaluation (TNED) Device for Aging Analysis of Carbon Steel SA 106B

The aging analysis of nuclear power plant components and materials is crucial for the power plant’s safety, and for its upgradation and life extension. The health monitoring of power plant materials, such as steel, is mostly done by employing conventional non-destructive evaluation (NDE) techniques. Considering the limitations associated with conventional NDE techniques, thermoelectric power based NDE (TNDE) is being explored. This paper presents the first ever development and successful demonstration of TNDE in Pakistan. Moreover, the aging analysis of carbon steel grade SA 106B is also being presented for the first time. The extraction-steam pipe samples (new and aged) of SA 106B, from a 46-years-old operating nuclear power plant, are being analyzed and the sensitivity of TNDE to aging induced changes in microstructure is demonstrated. Further, the aged SA 106B is simulated via heat treatment and the equipment is calibrated for its utilization in the nuclear power plant. The correlation between thermoelectric power (TEP) and Vicker hardness (HV) [TEP (µV/oC) = ~2x10-5 Hardness (HV) + 0.008] has been analyzed. It is revealed that the technique has the potential to analyze mechanical properties of aged extraction steam piping without cutting and taking the samples to the laboratory.