bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.