bluuebunny/crossref_metadata_2025 huggingface dataset:
Control Study for Automatic Startup of a Pressurized Water Reactor Nuclear Power Plant
Authors: Zhang Qi , Yu Wenhao , Xiao Longhao , Sun PeiWei , Wei Xinyu
Published: November 2024

To improve the automation for the startup process of a pressurizer water reactor (PWR) nuclear power plant (NPP), reduce the work intensity of the reactor operator, shorten the startup time, and improve the correctness and standardization of the process, the automatic control of NPP startup process is studied in this paper. First, the control scope and the requirements of automatic startup control system are analyzed and studied based on the characteristics and the operation management process of PWR NPP. Aiming at the current manual control tasks, three realization control methods are proposed, including sequence control, traditional closed loop control and advanced algorithm. A simulation platform for automatic startup system of typical PWR NPP was established based on 3KeyMaster, Relap5, and MATLAB/Simulink. Two typical manual control processes in state 3 for the startup process are selected to design the automatic control system based on PI controller and fuzzy controller respectively, and the simulation tests are carried out. The simulation results show that the designed control systems can realize the automatic control of the processes. Compared with the manual control, the control performance is improved, and the operation steps and work burden of operator are reduced. This study can provide a reference for the design of automatic startup and shutdown control system, and it is of great significance for improving the automation of startup and shutdown process of PWR NPP.
Stress Intensity Factor Solutions for Circumferential Through-Wall Cracks Applicable to Pool Type Sodium Cooled Fast Reactors
Authors: Yada Hiroki , Takaya Shigeru , Machida Hideo
Published: November 2024

ASME Code Case N-875 provides a rational in-service inspection (ISI) approach for liquid-metal cooled reactors. This is a risk informed approach and ISI requirements including acceptance criteria are established by considering features of respective plants in terms of the effects of flaws on the plant safety. In this approach, fracture mechanics is essential. Not only part-through-wall cracks but also through-wall cracks need to be evaluated, for example, to determine the maximum allowable size of flaws in reactor internal components and investigate the applicability of continuous leakage monitoring to flaws in sodium-retaining components. The basic procedure of the Code Case has also been incorporated in Fitness-for-service code (Section XI, Div. 2) in ASME, which provides requirements for Reliability and Integrity Management programs for nuclear power plants, including advanced reactors. The demonstration sodium-cooled fast reactor currently under development in Japan is expected to be designed with thin wall and large diameter to reduce the thermal stress. For some components, the ratio of radius to thickness is expected to exceed 100. There are currently no generalized Stress Intensity Factors (SIFs), which are required for fracture mechanics, applicable to components with such a large ratio of radius to thickness. In this study, as a part of the development of a flaw evaluation method applicable to components with large ratio of radius to thickness, the conservatism of applying the SIF solutions for a through-wall crack in a plate to a circumferential through-wall crack in a cylinder with large ratio of radius to thickness was discussed. As a result, it was clarified that the SIF solution for a plate should not be used for circumferential through-wall cracks. Therefore, a new SIF solution of a circumferential through-wall crack in a cylinder was developed by using Finite Element Analysis by Abaqus.
Study on Neutronics Properties of Zirconium Hydride Moderator in Reactor
Authors: Shixin Lin , Zheng Youqi
Published: November 2024

The metal hydride ZrH1.6 has a high hydrogen atom density, equivalent to that of liquid water. Compared to other hydrides (such as H2O and organic compounds), particularly at high temperatures, ZrH1.6 exhibits good strength and stability. Using hydrogen zirconium in a reactor can effectively slow down fast neutrons, thereby increasing the utilization of thermal neutrons and enabling the use of low-enriched uranium fuel. However, as a Solid state moderator matrix, hydrogen zirconium has a relatively large positive temperature coefficient in a reactor, posing a threat to the inherent safety of the reactor.

The goal of this study was to find out why the temperature coefficients of zirconium hydride is positive and to determine when the moderator and fuel temperature coefficients become positive. These are analyzed in this article by simplifing fuel and moderator calculation models and applying the SARAX program, from the most fundamental physical theory, the neutron physics parameters such as reactivity changes, nuclear cross-sections, neutron energy spectra, and reaction rates of the simplified model before and after temperature rise. The results indicate that the positive temperature coefficient of metal hydride ZrH1.6 is due to the hardening of the neutron energy spectrum below 1eV when the fuel and ZrH1.6 are heated, causing some neutrons to escape from the moderator and enter the fuel, thereby increasing the fission of the fuel. Moreover, when the reactor is designed in the under moderated zone, the temperature coefficient of fuel and moderator can be guaranteed to be negative, thus ensuring the safety of the reactor.
Scaled Experiment on Flow Characteristics of a Single Area of Spent Fuel Dry-Storage System of HTR-PM1000
Authors: Wang Jinhua , Wang Mengyao , Gong Menghang , Li Yue , Wu Bin , Zhang Wei , Gao Lihua , Ma Tao , Wang Haitao , Liu Bing
Published: November 2024

China is promoting the project of a 1000MW high-temperature gas-cooled reactor based on the technology of the pebble-bed module (HTR-PM1000). The spent fuel dry-storage system of HTR-PM1000 is divided into six regions, with each region containing multiple storage silos. The spent fuel canisters are vertically placed in the storage silos, where air is forced or naturally circulated to remove decay heat. Considering the cost of experiments and other factors, a 1/5 scaled experiment was conducted in this paper. The flow distribution and total resistance characteristics of different silos were recorded under the conditions of different fans and canisters loading. In order to eliminate the influence of fan performance on the experiment, two fans were operated at full-load separately, as well as the condition of two fans operating in parallel at full-load. To find the worst case and study the influence of the number of canisters on flow distribution, experiments were conducted for three operating conditions: no-load, full-load, and half-load. The results showed that the trend of silos flow variation was similar between no-load and full-load conditions. Under the half-load condition, the flow decreased with the increase of the number of storage canisters. Additionally, based on the total resistance characteristics, the resistance coefficients under the no-load and full-load conditions were obtained, which matched well with the previously calculated values.
Research on Human Reliability Analysis in Digital Main Control Room of Nuclear Power Plant
Authors: Wang Shuting , Zhao Ling , Liu Dalin , Yan Zhen
Published: November 2024

With the development of computer technology, nuclear power plant instrumentation and control systems have evolved from analog to digital control, and the introduction of new technologies has changed the contextual environment of information display, procedure, and control, thus requiring consideration of new human factors issues that may be brought about as a result. Human Reliability analysis in digital control systems is a key issue related to the safety of nuclear power plants, and there is no precedent for reference or experience to be gained. There is no precedent for reference nor experience to learn from, therefore, how to systematically carry out the human reliability analysis in digital control system is a hot and difficult issue in the current academic and engineering research. This paper discusses the design difference between digital main control room and analog technology main control room with the background of digital nuclear power plant, and at the same time explains how the human reliability changes in the digital environment, classifies the human error modes in the digital main control room system, and carries out a simple qualitative and quantitative analysis of the HRA of digital nuclear power plant based on a specific power plant.
Advanced Radioactive Material Removal System by Silver Zeolite (7) Evaluation of Noble Gas Adsorption Characteristics
Authors: Ishikawa Yoshihiro , Endo Koji , Narabayashi Tadashi , Kawahara Yasuhiro , Nakasaka Yuta
Published: November 2024

It is well known that it is difficult to remove noble gases generated during a nuclear accident. Currently, a noble gas holdup system made from activated carbon is being used. However, activated carbon has many shortcomings, such as flammability, non-moisture resistance, small absorption capacity et al. We have developed a new silver zeolite, “XeA,” as a noble gas adsorbent. The evaluation result shows that the holding time of this new absorbent for xenon is 480 minutes in dry air. This result is 80 times longer than that of activated carbon. Due to its excellent adsorption characteristics, it is expected to replace activated carbon in the vent system.

For this reason, there is a possibility that the volume of the existing system using activated carbon can be reduced to 1/80. Meanwhile, we also propose a system combining this XeA with a filter that does not transmit xenon.
Effects of Magnetic Ordering on the Ground-State Energy of Plutonium Dioxide: A Study Using Adiabatic Connection Fluctuation-Dissipation Theory
Authors: Nakamura Hiroki , Machida Masahiko
Published: November 2024

Efficiency and safety improvements in nuclear fuel development demand a comprehensive understanding of the thermophysical properties of plutonium dioxide. Because of the experimental difficulty of directly measuring these properties at high temperatures, they have been supplemented by numerical simulations, such as density functional theory (DFT). While DFT struggles to accurately replicate the nonmagnetic insulating ground state of plutonium dioxide, a method called DFT+U can reproduce nonmagnetic insulating state. However, the obtained state remains more unstable than magnetic states and is not the ground state. Adiabatic connection fluctuation-dissipation theory (ACFDT) is expected to be a promising solution, addressing higher-order correlations and exact exchange energies. In this study, we evaluate the energy of the ground state using ACFDT calculations with the random phase approximation and find that the nonmagnetic state can be more stable than the magnetically ordered state. This result successfully reproduces experimental observations of the nonmagnetic ground state of plutonium dioxide and has the potential to improve the accuracy of predicting the thermophysical properties of nuclear fuel materials.
Feasibility Study of Variable Spectrum Molten Salt Reactor by Adjusting Fuel Salt Compositions for the LiF-BeF2-ZrF4-UF4 System
Authors: Aizawa Naoto , Shishido Hiroki , Fujikura Koji
Published: November 2024

The Variable Spectrum Molten Salt Reactor (VSMSR) is proposed to produce a variable neutron energy spectrum field in the core. The feasibility of the VSMSR is investigated by adjusting the fuel salt composition for the LiF-BeF2-ZrF4-UF4 quaternary system. The available fuel salt compositions are reviewed on the basis of past thermophysical studies, and the candidate fuel salt compositions are determined. The neutronics analyses are performed to examine the feasibility of VSMSR from the aspects of criticality and neutron energy spectrum. The criticality analyses show that more than 5 mol% UF4 and more than 15 wt% 235U enrichment are necessary to satisfy the criticality criterion, and the fractions of (LiF+ BeF2), as well as the UF4 fraction, have a significant influence on the infinite neutron multiplication factor kinf. Also, the neutron energy spectrum is strongly affected by the fractions of UF4, LiF, and BeF2. The calculated thermal neutron flux below 1.1 eV has a difference of about 104 order among the candidate compositions, and the present study concludes that the adjustment of the fuel salt composition is very applicable to change the neutron energy spectrum of an MSR.
Neural Network-Driven Methodology for Predictive Health Monitoring and Aging Management in Nuclear Power Plant Operations
Authors: Cancemi Salvatore Angelo , Angelucci Michela , Lo Frano Rosa , Paci Sandro
Published: November 2024

In the nuclear power plant field, ensuring the safety and longevity of critical components like Class I/II components is paramount. Long-term operation (LTO) and aging management of nuclear power plants (NPPs) are critical for ensuring safe, reliable, and sustainable energy production. LTO involves systematic evaluation and maintenance to extend a plant’s operational life beyond its initial design period. The proposed study introduces a novel approach leveraging neural networks, for feature extraction in predicting the health status of Class II pipe. The methodology uses a digital twin of the pipe, which provides a synthetic dataset crucial for the training of the neural network. The digital twin, represented as a Finite Element (FE) model of a Class II pipe, simulates progressive degradation caused by thinning. This model effectively mirrors the gradual wear and tear experienced by the pipe over time, providing a realistic representation of its aging process. The architecture of the neural network efficiently condenses the input data (sensors readings) into a single, comprehensive signal. This output signal can be considered as the health status of the pipe, and it is then used to forecast the Residual Useful Life of the component. The combination of a digital twin, neural network-based feature extraction, and advanced forecasting techniques provides a comprehensive and reliable tool for assessing the health of nuclear components.
Numerical Simulation Study on Cladding Blister of Parallel Fuel Plates Under Subcooled Boiling
Authors: Liu Kexin , Xu Jianjun , Ding Ming , Cao Xiaxin
Published: November 2024

Plate fuel elements have the characteristic of more efficient heat transfer and are a potential new type of fuel assembly. As the fuel consumption of the reactor deepens, fission gas continues to accumulate and may enter between the cladding and fuel meat, causing cladding blister. In the case of subcooled boiling flow of coolant, the thermal-hydraulic characteristics of plate fuel elements after cladding blister have received little attention. However, cladding blister is a problem that must be faced in the design and development of fuel assemblies. This paper investigated the thermal-hydraulic characteristics of plate fuel elements after cladding blisters using numerical simulation methods. The research results showed that the cladding blister significantly impacts the wall temperature and void fraction distribution of the blister cladding, the blister back cladding, and the blister opposite cladding. The fuel meat temperature under the blister coverage area significantly increased. Further research revealed the effects of blister area, height, and number on plate fuel elements.
Investigation of Long Short-Term Memory-Based Models for the Forecasting and Diagnosis of PWR LOCA Progression
Authors: Obra Johndel , Miwa Shuichiro , Okamoto Koji
Published: November 2024

In the aftermath of the Fukushima-Daiichi accident, there has been a continuous effort to improve the existing LOCA management tools. One of the ways to improve this is to help the operators assess the situation more accurately by providing them with real-time LOCA diagnostic tools. The potential of Long Short-Term Memory (LSTM) models in LOCA forecasting and real-time diagnosis was investigated. The LSTM models were trained and evaluated using simulated LOCA scenarios from RELAP5/SCDAPSIM. LSTM forecasting was first verified through the accurate prediction of the temporal progression of the water level, primary coolant inlet flow rate, and hot leg temperature after the onset of LOCA. Then, the study established the capability of LSTM to diagnose the pipe break location and extent of pipe damage using small portions of data near the onset of LOCA. Lastly, the study demonstrated the potential of realtime LOCA diagnosis by combining the LSTM-based forecasting and predictive models.
Tube Plugging Induced Temperature Non-Uniformity in Once Through Steam Generator
Authors: Luo Yunhao , Xie Xiaoyang , Li Xiaowei , Wu Xinxin
Published: November 2024

Multi-layered helically coiled tube bundles are used as heat-transfer area of High Temperature Gas-cooled Reactor-Pebble Module (HTR-PM) Once Through Steam Generators (OTSGs). Tube plugging is very common in Pressurized Water Reactor (PWR) steam generators. Though the corrosion in HTGR OTSG is not so severe as that in PWR SG, it has large possibility to plug tube. However, plugged tubes will lead to temperature non-uniformity in the shell side helium in the steam generator. Large temperature non-uniformity will raise the tube working temperature. Excessive temperature non-uniformity will also lead to high thermal stress of the tube sheet. Thus, a precise prediction of temperature distribution after tube plugging is essential for the safety and efficiency of HTGR OTSG. An in-house code for tube and shell side coupled thermal analysis of HTGR SG was developed based on porous media assumption. This code is used to simulate the tube and shell side temperature distributions after plugging 1 or 2 tubes. The effect of molecular diffusion, turbulent diffusion and the dispersion are considered using the effective thermal diffusivity, which is normalized as the Peclet number (Pe). However, Pe of cross flow over tube bundles varies a lot in previous investigations. Thus, in the current investigation, a large range of Peclet number values are used for simulation. When Pe increases, outlet steam temperature non-uniformity become more severe. Numerical simulation shows that plugging one tube of inner layer will lead to more severe temperature non-uniformity than that of outer layers. The temperature non-uniformity will become obvious when the number of plugged tube increases. The code is validated using experiments carried out on the Engineering Test Facility for Steam Generator (ETF-SG) of HTR-PM. Experiments were performed for cases with 1 or 2 tubes plugged. Comparison shows that the present code can predict the experimental measurements well.
Research on Key Parameter Prediction Technology of Small Modular Pressurized Water Reactor Under Ocean Conditions
Authors: Cheng Yiheng , Li Tong , Tan Sichao , Wang Bo , He Zhengxi , Tian Ruifeng
Published: November 2024

Small modular pressurized water reactors can currently be installed in floating equipment such as ships and submarines. However, due to factors such as waves and grounding, their operation process is complicated. Long-term prediction of key transient parameters is required. By predicting the reactor over a long period of time Whether the operating status is normal, so as to take measures to correct its operating status and effectively reduce safety accidents, which helps to improve its safety. This article is based on the modeling of a small modular pressurized water reactor designed by Harbin Engineering University. During the modeling process, this article uses Multiple heat transfer tubes of a DC steam generator are used instead of multiple heat transfer tubes to simulate the heat transfer process, which simplifies the model, speeds up calculations, and improves transient accuracy. Later, the relap5 program developed under ocean conditions was used to simulate the rocking under ocean conditions. After obtaining the constant power transient data, this paper made predictions based on the data under ocean conditions. Because under undulating conditions, the key parameters in the reactor are affected by periodic gravity. Field-driven influences exhibit cyclical fluctuations. The greater the fluctuation amplitude, the larger the fluctuation period, the higher the parameter fluctuation range, the greater the power. The sensitivity of key parameters to changes in ocean motion conditions also increases. At present, predictions for small modular pressurized water reactors mostly stay at the direct analysis of data. Forecasting, since steady-state operating data has periodic fluctuations, there is a certain mathematical relationship between data and time. In order to improve the accuracy of forecasting, a joint model of VARIMA model and LSTM is proposed. Among them, VARIMA model can not only capture the trend changes of data, but also Can handle linear and periodic data. LSTM is a long short-term memory network that effectively captures the important long-term dependencies in the sequence and solves the gradient problem by introducing the concepts of memory cells, input gates, output gates and forgetting gates. Therefore, after dividing the data into periodic data and non-periodic data, VARIMA is used to predict the periodic data, and LSTM is used to predict the non-periodic data. After adding the two prediction values, the prediction result of the model is obtained., and compare the prediction results of the VARIMA+LSTM joint model with the optimized LSTM model to verify whether it can improve the prediction results.
Research on Inverse Reconstruction Method of Radiation Dose Distribution Based on RBF Interpolation
Authors: Hu Jifeng , Liu Yongkuo , Chen Zhitao , Qian Xinrong
Published: November 2024

The precise reconstruction of radiation dose fields holds paramount significance in radiation protection during the decommissioning of nuclear facilities. In this study, a radiation field reconstruction method based on sparse scattered monitoring data was employed, utilizing Radial Basis Function (RBF) interpolation. We conducted a detailed analysis of both RBF interpolation and its compact support variant and validated their reconstruction effectiveness through four sets of simulation experiments. The first two experiments simulated unshielded environments with a single radiation source and multiple radiation sources, respectively. The subsequent two experiments simulated shielded environments with a single radiation source and multiple radiation sources, respectively. The reconstructed results were compared with those obtained using the Kriging method, with 2.5% of all nodes utilized as measurement data in the experiments. The four sets of experiments utilizing RBF interpolation and compact support RBF interpolation exhibited commendable reconstruction performance, with average relative errors of 1.06%/3.52%, 1.13%/2.56%, 8.38%/11.59%, and 3.98%/5.65%, respectively. In comparison to the Kriging method, RBF interpolation consistently outperformed in all experiments. It is worth noting that although the overall performance of compact support RBF is relatively inferior to that of the Kriging method, its reconstruction accuracy at the locations of radiation sources surpasses that of the Kriging method. These research findings provide preliminary evidence of the potential application prospects of RBF interpolation in radiation field reconstruction.
Analysis of Common Cause Events in Multiple Reactor Site Initiation Events
Authors: Feng Wanxin , Li Zecong , Yu Yu , Niu Fenglei
Published: November 2024

The overwhelming majority of nuclear power plants in operation or under building in the world contain more than one reactor. After the Fukushima nuclear accident, more and more scholars and regulators have begun to pay attention to the risk of multi-unit sites. This paper first discusses the significance and development history of probabilistic safety analysis for multi-unit sites, and analyzes that the frequency analysis of initiating events is still insufficient. The event of multiple reactors occurring at the same time can lead to more serious risk consequences, which need to be analyzed, especially focusing on the shared equipment, common cause failure, and other reasons. The analysis shows that the original redundancy system still exists in the multi-unit site, and the redundancy system between units should also be considered. During the analysis of the initiating event, the common cause failure groups that need to be established are of the frequency type, and the existing methods of building fault trees are not applicable. This paper discusses how to calculate the common cause failure problem in the fault tree model when the same initiating event occurs in multiple units at the same time.
Neutronic and Thermal-Hydraulic Analysis for Small Solid-Liquid Dual-Fuel Reactors With Different Fuel Elements
Authors: Qiu Zhichao , Zhuang Kun , Wang Yongzhan , Wang Xiaoyu , Deng Lina , Wang Yingzheng , Wang Sipeng
Published: November 2024

Small molten salt reactor (SMSR) is an advanced nuclear energy system with the advantages of high safety and high thermoelectric efficiency. Due to its good development prospect, a new type of cross-shaped spiral fuel has been widely researched around the world. In this paper, the cross-shaped spiral fuel was applied to a small liquid-solid dual-fuel molten salt reactor proposed in our previous study, and the neutronic and thermal-hydraulic performance of the core were investigated respectively by OpenMC and COMSOL and compared with that of the conventional rod-shaped fuel. The results show that using cross-shaped spiralfuel slightly reduces the effective multiplication factor and that the safety performance of small liquid-solid dual-fuel molten salt reactor under accident condition is not significantly different between two fuel elements. The increasing of the rotational angle of the spiral cross-shaped spiral fuel plays an important role in changing the coolant flow and improving the heat transfer performance, the maximum core temperature of cross-shaped spiral fuel is reduced by 16.27% compared with that of the rod-shaped fuel. It can be conclude that adopting cross-spiral fuel can in a small solid-liquid dual-fuel reactors can improve effeciently the heat transfer performance but little affect the neutronics characteristics.
Preliminary Design and Analysis of a Very-High-Temperature Reactor-Driven Hydrogen-Electricity Cogeneration System
Authors: Ni Hang , Qu Xinhe , Zhao Gang , Wang Jie , Peng Wei
Published: November 2024

The very-high-temperature reactor (VHTR) is an innovative fourth-generation nuclear energy technology suitable for pairing with iodine-sulfur (IS) cycle for nuclear hydrogen production. This study designs a new VHTR-driven hydrogen-electricity cogeneration system integrating IS cycle and helium closed Brayton cycle, and studies the impact of key parameters on system performance. As the total compression ratio increases, the net output work of the system increases, and the energy utilization efficiency rises. As the split ratio increases, the heat sink loss of the auxiliary cooler (AC) increases, and the energy utilization efficiency decreases. As the reactor inlet temperature increases, the variation range of the split ratio is larger, causing a larger variation range of the hydrogen production rate. When the AC becomes a process heat supply unit, the hydrogen-electricity cogeneration system becomes a hydrogen-electricity-heat polygeneration system, whose energy utilization efficiency increases with the split ratio. At the reactor inlet temperature of 550 °C and the total compression ratio of 3.0, the variation range of the split ratio is 0.007–0.769. When the split ratio increases from 0.007 to 0.769, the energy utilization efficiency of the hydrogen-electricity cogeneration system decreases from 46.4 % to 23.2 %, while that of the hydrogen-electricity-heat polygeneration system increases from 47.5 % to 55.6 %.
Anomaly Detection of Thermal System Using CAE-DDQN Model
Authors: Li Tong , Cheng Jiahao , Wang Bo , Tan Sichao , Tian Ruifeng
Published: November 2024

In the development of the international nuclear power industry, nuclear energy safety has always been an unavoidable major issue, and nuclear energy safety concerns global energy development, public safety and national security. In the process of variable operating conditions of the thermal engineering system, incorrect alarm information may appear in the main control room due to the fluctuations of the system operation, which hinders the judgment of the actual operating state of the thermal engineering system. Although some research work has been carried out in the field of thermal system condition recognition in the world, the relevant research often adopts unsupervised machine learning model or supervised neural network model, and the application of deep reinforcement learning method is rarely studied. The deep reinforcement learning model can interact in real time in the environment, and multiple neural network models inside the deep reinforcement learning model can be trained through the guidance of the designed reward function to output different result values that can better complete the task based on different data. This method often has strong dynamic programming characteristics. It has strong theoretical feasibility in dynamic adjustment of spatial coding of thermal system data. Therefore, by combining the Convolutional Auto-Encode (CAE) method in the auto-encoder and the Double Deep Q-learning (DDQN) method in the deep reinforcement learning, this research constructs an intelligent model for the recognition of abnormal working conditions of thermal systems under time series. This research takes the simulation data of Qinshan Nuclear Power Plant under varying working conditions as the data sources, and compiles the spatial encoding of the time series data through the Convolutional Auto-Encode (CAE) model, which is pre-trained. Take the constructed spatial coding as the state value of the Double Deep Q-learning (DDQN) model, and guide the Double Deep Q-learning (DDQN) model to adjust the state of spatial coding through appropriate reward value. Finally, the ideal test results are obtained. By comparing with traditional anomaly detection methods, it is proved that CAE-DDQN model has a very ideal condition recognition effect, and the recognition effect of difficult to identify data and very early accident data is much stronger than that of traditional machine learning or deep learning methods. At the same time, it is proved that the deep reinforcement learning method based on the principle of Q function is feasible in the condition recognition of thermal engineering system.
Neutronics Design of Molten Salt Reactor for Transmutation of Various Radioactive Nuclides
Authors: Fujikura Koji , Aizawa Naoto
Published: November 2024

The graphite-moderated fluoride molten salt reactor for simultaneous transmutation of MA (minor actinides) and FP (fission products) is proposed in the present study. In this core concept, MA is transmuted in the core region by mixing MA nuclides into the fuel salt; FP is transmuted in the reflector region surrounding the core by introducing the FP-containing molten salt different from the fuel salt. Two types of MA-loading fluoride fuels are considered: the uranium fuel with TRU nuclides (U+TRU fuel) and with MA nuclides (U+MA fuel) from PWR spent nuclear fuel. The FP nuclides considered in this study are the soluble LLFP nuclides: Se-79, Zr-93, Sn-126, I-129, and Cs-135, and the dominant heat generation nuclides: Sr-90 and Cs-137. The criticality and burnup calculations are performed using Serpent2, and the feasibility of simultaneous transmutation of MAs and FPs is investigated. The burnup analyses show that the transmutation amounts of Se-79, Sr-90, I-129, and Cs-137 in the FP salt are larger than the production amount in the fuel salt. The amount of MA transmutation in total for U+MA fuel is larger than that for U+TRU fuel and exceeds the MA production amount in a typical LWR.
Research on Interpretability Techniques in Two Widely Used Neural Network for Nuclear Power Plant Fault Diagnosis
Authors: Liu Jie , Macián-Juan Rafael
Published: November 2024

In the domain of nuclear power plant operations, effective and reliable fault diagnosis is crucial for ensuring safety and operation efficiency. However, the black-box properties of machine learning models limit their practical application in the field of nuclear power fault diagnosis. This study uses the Shapley value theory in game theory to analyze the reasoning process of the fault diagnosis model, and combines the attention mechanism to propose an innovative and advanced nuclear power fault diagnosis model whose reasoning process is interpretable. A series of experiments based on simulation data were conducted and compared with baseline methods, proving that the method proposed in this study can effectively improve the performance of the nuclear power fault diagnosis model. This research can provide an important reference for the application of machine learning methods in nuclear power fault diagnosis.
CFD Simulation of Water Hammer Induced by Condensation Using an Improved Phase Change Model
Authors: Peng Li , Tu Ruiyang , Lin Daping , Xiong Feng , Zhao Jiaming , Long Xiaoxin , Chen Zhengyu , Zhang Zihao , Guo Wentao , Wang Shengfei
Published: November 2024

A computational fluid dynamics (CFD) based approach using FLUENT is applied to investigate the phenomenon of condensation-induced water hammer (CIWH) caused by direct contact condensation. In these simulations, the two-phase interface is explicitly tracked using the volume of fluid (VOF) method, and a hybrid heat transfer coefficient (HTC) model based on surface renewal theory is employed for interface heat transfer. The HTC model is implemented through user-defined functions (UDFs). The simulation results are compared with experimental data obtained from the PMK-2 facility of the VVER-440/312-type nuclear power plant in Budapest, Hungary. The results demonstrate that the model aligns well with the measured temperature field, as well as the timing and magnitude of pressure peaks. This CFD approach can be utilized for prediction and prevention of CIWH.
Few-Shot Learning for Abnormal Event Detection in Nuclear Power Plants
Authors: Zhang Tianhao , Guo Chao , Jia Qianqian , Huang Xiaojin
Published: November 2024

Recent research on abnormal event detection in nuclear power plants (NPPs) increasingly adopts the data-driven method, aiming to identify anomalies by harnessing vast operational data without needing prior knowledge of plant system dynamics. However, the limited availability of abnormal samples within NPPs affects the detection accuracy of data-driven methods. This paper introduces a few-shot learning approach based on neural networks to develop an efficient anomalous event detector using sparse samples within NPPs. To achieve this, a neural network structure is employed for operational feature extraction to facilitate the transformation of input data into lower-dimensional vectors with embedded space. Then, a prototypical network is introduced to derive a prototype for each abnormal event class based on a few embedded vector training samples. In this way, the anomaly type in a new instance can be identified by comparing the similarity of its embedded vector to each prototype. Experimental results illustrate that the proposed approach achieves high abnormal event detection accuracy.
Underlying Deep Learning Networks Diagnosis Evaluation and Generative Adversarial Network Data Augmentation Based on a Benchmark Accident Dataset
Authors: Qi Ben , Wang Yu , Xiao Xingyu , Liang Jingang
Published: November 2024

Nuclear energy plays an important role in the global energy supply, particularly as a key source of low-carbon electricity, while ensuring the safe operation of nuclear power plants (NPPs) is crucial. Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has been increasingly interested assisting operators in NPP accident diagnosis. However, the field has lacked a benchmarking effort for underlying deep learning networks based on public datasets for accident diagnosis, as well as research on time-series data augmentation efforts for accident data. Based on the first open-source NPP accident dataset NPPAD, which covers all kinds of accident data of pressurized water reactors and has been validated with Fukushima nuclear accident data for reliability, this study explored accident diagnostic techniques based on Multilayer Perceptions (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Transformers, and data augmentation techniques based on conditional Generative Adversarial Networks (cGANs) and Time-series Generative Adversarial Networks (TimeGANs). Experimental results show Transformers have the best classification ability for accident types. In addition, the standardization of data has a large impact on the classification effect, and standardization in data preprocessing will make all models substantially more effective. For data augmentation, TimeGANs exhibit higher performance compared to cGANs in generating fake datasets and can better capture the dynamic features of time-series data. This paper provides a reference for the application of deep learning through the study of three parts: a comprehensive comparison of underlying deep learning networks, data standardization processing, and data augmentation.
Impact of Synoptic Weather Patterns Along the Pacific Coastline of Japan on Tornado Wind Hazard Curves
Authors: Fujiwara Kota , Nohara Daisuke , Eguchi Yuzuru , Hattori Yasuo , Hirakuchi Hiromaru
Published: November 2024

The present study focuses on the wind hazard assessment along the Pacific coastline of Japan by categorizing tornado data based on synoptic weather patterns. The region faces unique challenges, including tornadoes occurring over the sea and a restricted area of interest along the coastline, leading to potential sampling issues. To address these challenges, the TOrnado Wind speed hazard for Limited Area (TOWLA) model was employed. Tornado data were classified into seven synoptic weather patterns. The hazard analysis conducted for each of these synoptic weather patterns along in the Pacific coastline region revealed that extra-tropical cyclones, typhoons, and monsoon (summer) were significant contributors to high-risk events. Monsoon (winter) had the highest tornado counts but lacked severity. Typhoons, exhibiting a distinct formation mechanism, presented a unique hazard factor along the Pacific coastline, which require verification through comparisons with other areas.
Experimental Study in Cooling Rate by Containment Spay
Authors: Wang Ning , Yang Yapeng , Liu Zhe
Published: November 2024

In case of loss of coolant accident, temperature and pressure in the containment would rise due to evaporation of coolant. Besides, radioactive aerosol and gas could possibly release to the containment in case of severe accident. Containment spray system is designed to cool it down, reduce its pressure and remove airborne radioactive material. According to NUREG 1465, 95% of released fission product exists in form of aerosol. Therefore, one experimental apparatus for typical China three loop PWR was built to investigate the above phenomenon. It consists of 4 parts: 1) spray vessel, which simulates reactor containment; 2) Spray system; 3) measurement system including temperature, pressure and flow rate; 4) Water tank, which stores and provides spray water. The spray vessel is 2.0m high with an internal diameter of 1.0m. There are 9 temperature sensors inside the spray vessel. The maximal design pressure of the vessel is 0.8MPa and maximal design temperature is 180°C. The spray tank is 1.0m high with an internal diameter of 1.0m. Temperature is the key factor that influence the behavior of aerosol, so the cooling rate experiment was conducted before the aerosol removal coefficient experiment. In this paper, experiments were conducted to obtain cooling rate by containment spay, which was the basis for aerosol removal coefficient experiment. Temperature distribution of spray vessel during the spray was obtained and cooling down rate was measured. Results show that spray flow rate and spray water temperature are the main parameters that determines cooling down rate. In future, aerosol spray removal coefficient will be obtained.
Research on the Localization Method of Loose Components in Nuclear Power Plants Based on Hilbert-Huang Signal Processing
Authors: Jiang Jiming , Liu Yu , Lu Daogang
Published: November 2024

During the service cycle of the nuclear power plant, due to factors such as construction renovation, irradiation, and other factors, loose parts may transfer along the flow of coolant in the primary circuit, which may lead to the failure of the RCP system and even cause the failure of the primary circuit containment state. The Loose Part Monitoring System (LPMS) is used to detect, locate, and estimate possible loose parts in the reactor primary circuit. To accurately locate the position of loose components, identify and track their trajectory within the system, and facilitate subsequent maintenance and troubleshooting, this paper applies the Hilbert Huang transform to extract the main frequency of the impact signal to achieve accurate measurement of the signal arrival time. Due to the unique advantages of its nonlinear and adaptive signal decomposition method, it can separate the amorphous vibration waveforms in the signal with the highest confidence. By determining the peak time of the first envelope curve after Hilbert transformation, the accurate measurement of signal arrival time can be realized conveniently. Based on the triangulation method, the time difference of the sensors obtained can accurately locate the impact location. To verify the effectiveness of the method, the algorithm validation is carried out on a small model, and the results show that the proposed localization method based on Hilbert’s yellow transform achieves significant results in the localization of impact signals, and can achieve fast localization. Finally, a digital twin system is proposed based on simulation to assist the LPMS system for fast localization by iteratively obtaining a collision moment lookup table.
Investigation on Development of New Mechanistic Model for Post Dryout Heat Transfer
Authors: Xia Zihan , Cheng Xu
Published: November 2024

During some transient scenarios such as loss of coolant accident (LOCA) in nuclear power plants, dryout could occur in fuel assemblies. The heat transfer after dryout will determine the maximum cladding temperature when dryout occurs. Therefore, identifying the heat transfer mechanism and predicting heat transfer in Post-dryout (PDO) region will be particularly important for facility safety operation. In the post dryout region, the behaviour of droplets may significantly affect the heat transfer and subsequently, the wall temperature. When droplets move toward the wall due to turbulence dispersion effect, most of the droplets will be completely evaporated inside the vapor layer or bounced back to the mainstream. Therefore, in this paper, the flow area is divided into the central region and the near-wall region considering the large difference in vapor superheating between wall and tube center. The interfacial heat transfer is calculated separately in these two regions with a correlation of vapor temperature profile obtained from CFD results. There will be droplet mass exchange between the two regions due to the concentration difference caused by the fluctuation velocity profile. The calculation results were compared with Becker’s water PDO experimental results, and it was found that the predicted wall temperature were in good agreement with the experimental data. It shows that the new model can improve the prediction accuracy of heat transfer in the post dryout region.
Performance Analysis for Enhanced Cr-Coated Fuel Cladding System in APR-1400 Reactor
Authors: Alzarooni Asma , Alrwashdeh Mohammad , Alameri Saeed A.
Published: November 2024

The conventional Zircaloy-uranium fuel-cladding system in pressurized water reactors faces challenges such as reduced mechanical strength, stability, and susceptibility to oxidation and embrittlement at high temperatures. The Accident Tolerant Fuel concept, aiming to address these issues, involves replacing or coating cladding with materials exhibiting enhanced corrosion resistance. This study explores the efficacy of ATF using a chromium coating and selecting an adhesive layer material (Niobium or Molybdenum) between cladding and coating. Neutronics analyses, employing the Serpent Monte Carlo 2.31 simulation tool on the APR-1400 reactor, assess the impact on different neutronics parameters. A sensitivity analysis involves varying coating thickness (10 μm, 15 μm, 20 μm) and adhesive layer thickness (2.20 μm, 3.20 μm, 4.20 μm) for Zircaloy cladding. Results show that adjusting ATF design parameters can match the original fuel-cladding cycle length. The 10 μm coating with 2.2 μm Nb adhesive layer exhibits minimal neutronics impact, suggesting it as an alternative cladding material. In 2D reactor core analysis, Nb outperforms Mo due to a lower reactivity difference and smaller absorption cross-section, enhancing thermomechanical properties with minimal neutronics impact. Implementing Cr-coating with Nb adhesive layers holds promise for enhancing safety and reliability in nuclear power plants, particularly in mitigating risks during high-temperature conditions and loss-of-coolant accidents.
Adaptive Refinement Simulation Model of Condenser Based on the Mechanism-AI Coupling Modeling Paradigm
Authors: Jing Furu , Xue Ruojun , Tang Jiaming , Chong Haofang
Published: November 2024

Condenser is widely regarded as the key equipment of nuclear energy utilization device, and its performance has an important influence on the safety, economy and stability of the whole nuclear energy utilization device. In this paper, aiming at the problems that the simulation accuracy of the condenser real-time simulation model is lower than that of the numerical calculation simulation method and it is difficult to realize self-adjustment according to the actual changes, the NSGA-III ( a multi-objective optimization algorithm ) related to machine learning in AI technology is introduced into the mechanism modeling method,, that is, a mechanism-AI coupling modeling method is adopted. The specific model construction process is divided into three steps : constructing the basic model of the condenser according to the fluid network method, using the AI algorithm to determine the reason for the reduction of the model accuracy, and coupling the mechanism model with the AI algorithm to construct the condenser adaptive refinement simulation model. Finally, the simulation of a condenser is carried out to verify the model. The results show that the mechanism-AI coupling model can further improve the simulation accuracy of the condenser under the condition of meeting the real-time simulation requirements. When the condenser parameters change, the model can be adjusted independently to reduce the error caused by the change of the condenser parameters.