bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Simulation of the Effect of Mixed Gas Temperature and Steam Injection Rate on Hydrogen Distribution at Steady State in Small Volumes
Authors: Feng Yu , Wang Hongliang , Yu Mingrui , Yuan Yidan 
Published: November 2022

Abstract
Hydrogen may be released into the containment atmosphere of a nuclear power plant during a severe accident. Locally, high hydrogen volume fraction can be reached that can possibly cause fast deflagration or even detonation and put the integrity of the containment at risk. The measurement accuracy of hydrogen volume fraction in containment is important to judge the flammability of hydrogen in the containment during a severe accident. The experimental device for hydrogen distribution and hydrogen volume fraction measurement at steady state in small volumes will be set up in the future, it is necessary to study on the effect of mixed gas temperature and steam injection rate on hydrogen distribution at steady state in small volumes.
Computational fluid dynamics (CFD) codes can be applied to predict hydrogen distribution in compartments at steady state in the containment during a severe accident. The pressure vessel is used to provide the same mixed gas as in the containment during a severe accident, and the numerical simulation is carried out for different work conditions with hydrogen-nitrogen mixture of 20% hydrogen volume fraction in the pressure vessel, investigating the effect of mixed gas temperature and steam injection rate on hydrogen distribution at steady state in small volumes. The numerical simulation results are qualitatively analyzed in the paper, the following conclusions indicate that it is no obvious stratification of hydrogen at steady state in small volumes at the work conditions (0–100, 0–120, 0–140, 0–160) with different mixed gas temperatures and it is no obvious stratification of hydrogen at steady state in small volumes at the work conditions (0.1–160, 1–160) with different steam injection rates. Therefore, mixed gas temperature and steam injection rate have almost no effect on hydrogen distribution at steady state in small volumes.
The numerical simulation results can provide some data basis for the experimental device for hydrogen distribution and hydrogen volume fraction measurement at steady state in small volumes in the future, and the outcomes of the qualitatively analysis can be used for design and optimization of the experimental device, in order to develop a hydrogen volume fraction measurement system for the containment during a severe accident.
Investigation of Model-Based Anomaly Detection Method for the Full-Power Natural Circulation Small Modular Reactor NHR200-II
Authors: Huang Canxing , Xiong Huasheng , Feng Junting 
Published: November 2022

Abstract
The NHR200-II is a full-power range natural circulation small modular heating reactor characterized with inherent safety. The significant difference between NHR200-II and a general pressurized water reactor is that the load of NHR200-II is relatively smaller, and it could vary rapidly in a wider range under different operation conditions. The control system of NHR200-II is required to sustain the stability of reactor operating parameters and make sure the reactor follows the load change in time.
Anomaly conditions may occur during the operation of NHR200-II. They may not induce safety-related accidents that gravely impair the reactor core or lead to the release of large-scale radioactivity, but they will fluctuate the power output. Therefore, it is practically valuable to detect abnormal working conditions in time. Furthermore, the detected information of anomaly is helpful for operators to conduct the reactor operation.
A model-based anomaly detection method to diagnose abnormal conditions of circulation pump sensor in NHR200-II is investigated in this paper and verified with the NHR200-II model. The simulation results show that the circulation pump sensor fault will cause the variation of running parameters, including inlet and outlet temperature of the coolant inside the reactor core, nuclear power, and output steam flowrate from the steam generator. The variation of parameters will lead to system deviation from expected operating conditions and system stability degradation. When the proposed anomaly detection method is applied, the circulation pump sensor fault could be successfully diagnosed and detected, and a fault-tolerant control strategy is deployed to correct the deviations and stabilize the operation of the NHR200-II reactor.
A Discrete-Ordinates Variational Nodal Method for Solving Multi-Dimensional Neutron Transport Equation With Unstructured Mesh
Authors: Sun Qizheng , Zhang Tengfei , Liu Xiaojing , Chai Xiang , Xiong Jinbiao 
Published: November 2022

Abstract
The variational nodal method (VNM) is an advanced nodal method in the field of reactor physics. The VNM solves neutron transport equations directly without transverse leakage interpolations used in other nodal methods. Moreover, the VNM can tackle arbitrary expansion orders. Given its benefits, VNM performs well in a variety of applications. However, with the advancement of nuclear science and technology, the ability to handle complex unstructured problems becomes indispensable.
In order to increase the geometric compatibility of the VNM, an unstructured-mesh variational nodal method (VNM) for solving multi-dimensional neutron transport equations is presented in this paper. The neutron transport equation is transformed into a variational formulation, in which an even-parity Lagrange multipliers enforce neutron conservation in each node, to obtain the weak-form solution. The entire problem domain is spatially meshed into several unstructured nodes. And the neutron flux, source, and current are spatially approximated by a set of complete orthogonal polynomials. The conception of the unique node is adopted to reduce the memory and improve efficiency. Orthogonal polynomials are established in a standard node for spatial discretization, and each unstructured node is related to the standard node by the coordinate mapping matrix, respectively. The discrete-ordinates (SN) method is employed to deal with angle variables so that the original equation is decoupled into several equations with different discretization directions. Then, the variational formulation is completely discretized, and the response equations are derived. Finally, different types of benchmarks are employed to validate and evaluate the proposed method. Numerical results demonstrate that unstructured-mesh VNM exhibits significant geometric compatibility and comparable accuracy.
Research on Smart Nuclear Power Construction Based on Digitalization and System Engineering in TNPS
Authors: Yang Qiang 
Published: November 2022

Abstract
In order to solve the digital transformation problems faced by Jiangsu Nuclear Power Station (TNPS) under the complex situation, and to benchmark advanced enterprises to improve the current digitalization level, TNPS established a special organization to carry out the V-type system construction method which covers user needs, functional requirements, architecture design, detailed design, code implementation, unit testing, integration testing, system testing, acceptance testing, etc. TNPS is carrying out the top-level design and application implementation of intelligent station, support platform and network construction and existing information systems based on the foundation functional and digital transformation, smart application and decision-making platform construction as the main body of the smart power plant implementation plan, and combed the application scenarios from the four dimensions of “personnel, equipment, environment, and operation”, and introduced smart nuclear power using smart construction sites and smart outage as examples Implementation content and solutions for typical applications. This work has advanced design and complete system, which has certain reference significance for nuclear power enterprises to carry out digital and intelligent transformation.
The Research on the Risk Monitor With Specific Plant Configuration at Shutdown Mode for VVER Nuclear Reactor
Authors: Dong Fangyu , Du Jinyan , Ma Chao , Deng Wei , Xu Qingqing , Zhang Rui 
Published: November 2022

Abstract
The research and application of Risk Monitor is one of the most influential analysis tool in the field of probabilistic safety analysis (PSA) of nuclear power plants. Risk Monitor is often used in nuclear power plant operation risk management, scheduled maintenance risk management or other plant activities, aims to help the formulation of power plant integrated decision-making from the risk-informed perspective. The VVER nuclear reactors have four redundant trains to achieve relatively higher safety level, compared to other nuclear power plants design. Because of its four redundant systems design, the configurations of certain system, particularly for the shutdown mode are various. This paper takes the VVER residual heat removal system (JNA) as an example and discusses the method of developing the average PSA model into a risk monitor PSA model for VVER JNA at shutdown mode. Meanwhile, this paper establishes the cases of different JNA setting configurations, aims to support the plant configuration risk management analysis from the perspective of risk level.
UTSG Water Level Prediction Model Based on LSTM and GRU Algorithms
Authors: He Qingyun , Li Bin , Peng Sitao 
Published: November 2022

Abstract
Steam generator water level is a complex thermal hydraulic system, which in-ludes nonlinearity, time-variant characteristic, non-minimun phase, mechanical limit and mesurement error. Particularly, “false water level” phenomenon caused by nonminimun phase greatly affects the control of steam generator water level. Thus, in order to greatly help PWRs operators control the water level, enhance the safety of PWRs operation and improve the economic of PWRs, it is of importance to study the technology on prediction of steam generator water level variation trend under normal operation condition of PWRs. A predictive model for U-tube steam generator (UTSG) is in need as an assistance for operators to better control the UTSG water level. The training data, i.e., the variation of UTSG water level in uenced by the sudden change of steam ow rate and feedwater rate, were obtained from a differential equation model, i.e., Ivring’s model. Long short-term memory (LSTM) and gated recurrent unit (GRU), as powerful time series prediction algorithms in deep learning eld, were used to train the UTSG prediction model. The result shows that the trained LSTM and GRU based predictive model have good performance on testing data set with both the one-step prediction and multi-step recurrent prediction. It can be used as a prototype of steam generator level prediction without the intervention of automatic control system.
Thermal Performance Monitoring and Analysis of Nuclear Power Plant
Authors: Hou Jianfei , Zhang Xun , Zimmermann Heino , Xiang Zhaohui 
Published: November 2022

Abstract
In order to avoid sudden fault and damage of equipment which will affect reliability and safety of the nuclear power plant, it is necessary to monitor the actual thermal performance of the nuclear power plant and analyse the thermal performance trends of thermal circuits and important equipment which is the basis of fault predicts. Based on the mature and widely applied EBSILON software, thermodynamic model of a nuclear power plant in operation is built including the primary loop, second and third loop and important equipment such as steam turbines, condenser, deaerator, low-pressure and high-pressure heaters and main feed water pumps. In particular, hundreds of instruments are taken into account in the thermodynamic model and all the measured data during operation of the nuclear power plant is imported into the instruments. Thermal performance of the nuclear power plant is evaluated and analyzed in different seasons and results displays the performance factors of important equipment compared to the design performance. In addition, a data reconciliation calculation is taken out to check the correctness of measurements using the massive redundant measured data of the plant and results shows some instruments may have potential measurement fault. Current work lays a foundation for the future research of fault predicts and diagnoses of the nuclear power plant.
Comparative Study on Internal Flooding Prevention Design of Several Types of NPPs and Improvement Suggestions
Authors: Yinghao Yang , Zhaohua Li , Xiaoyan Xu 
Published: November 2022

Abstract
There are many tapes of nuclear power plants (NPPs) in operation and under construction in China, involving M310 reactor, VVER reactor, AP1000 reactor and HPR1000 reactor and so on. The design concepts of each type nuclear power plant are quite different, including active nuclear power plant and passive nuclear power plant, second-generation reactor and third-generation reactor. Their designs for preventing internal flooding are also very different, either through physical isolation or by adding flood prevention barrier, but each reactor type nuclear power plant has its own advantages in flood prevention. In order to further improve the internal flooding prevention ability of nuclear power plants in China, this paper makes a comparative study on the internal flooding prevention design of several mainstream tapes of nuclear power plants in China, and gives the advantages of each nuclear power plant in the internal flooding prevention design. By drawing on the design advantages of other reactor type nuclear power plants and using the analysis method of probabilistic safety assessment (PSA), this paper quantitatively evaluates the improvement of the ability of reactor type nuclear power plant that our company participated in the design or construction to resist internal flooding risk after considering these design optimization, and gives targeted design improvement suggestions.
The Crown Predictive Model of Cold-Rolled Zirconium Alloy Strip Sheet Based on Machine Learning Algorithm
Authors: Cao Yuan , Cao Jianguo , Gao Yingqi , Wang Ben , Zhang Pengfei , Li Fang , Gao Bo 
Published: November 2022

Abstract
To solve the problem that the crown accuracy directly affects the profile and flatness of the zirconium alloy strip caused by the multi-schedule and more than sixty passes cold rolling of nuclear zirconium alloy strip. The crown predictive model is established based on support vector regress algorithm (SVR), and the parameters of the SVR algorithm are optimized by the SAPSO algorithm. Meanwhile, the crown predictive model based on the SAPSO-SVR algorithm shows that the relative coefficient of predicted values is higher than 0.94. Industry test demonstrate that the crown predictive model provides a new method for zirconium alloy strip shape control and an approach to optimize the control strategy. The proposed model provides a new method and idea for shape control and optimization research in the zirconium alloy plane rolling process.
Level 1 Probabilistic Safety Analysis (PSA) of TNPP Units 3 and 4
Authors: Yu Wenge , Li Youyi , Lang Xiye , Sun Yang , Zhao Pengfei 
Published: November 2022

Abstract
Tianwan Nuclear Power Plant (TNPP) Units 3&4 (the second phase) are a landmark project for Chinese and Russian governments to jointly promote China-Russia nuclear energy cooperation.All the reactor units adopt the Russian VVER-1000 improved nuclear power units which are now operated by project owner, Jiangsu Nuclear Power Corporation (JNPC).
The depth and quality of PSA development, at the stage of Units 3&4 commercial operation,should be sufficient to obtain the risk-informed insights, enough to support the Risk-Informed application of PSA, and also reflect the actual design and its operation experience of TNPP.
This paper presents the brief introduction of PSA of Unit 3&4 of TNPP (power operation mode). The important parts of this presentation are as follows:probabilistic safety indices, study scope, technical elements and the analysis of Core Damage Frequency (CDF) quantitative results.
Through quantitative calculation, the point estimation value of the total CDF of TNPP under the power condition is obtained. And the frequency of occurrence of each event sequence and the CDF contribution of each initiating event (IE) etc., are analyzed.
Based on the developed PSA models, four PSA application tools have been developed and applied in the risk monitoring activities of nuclear power plant. This PSA model is used to take the lead in carrying out the Online maintenance in China.
Parameter Calculation Method of Porous Media Based on BP Neural Network
Authors: Zhao Fei , Lu Daogang , Liu Yu , Liu Dong , Xu Jiliang , Wu Jinghui , Xie Fei , Wang Yuchao 
Published: November 2022

Abstract
There are a large number of equipment densely arranged in liquids in nuclear power plants, such as fuel assemblies, steam generator heat transfer tubes, spent fuel storage and transportation racks, etc. These equipment are complex in shape and compact in arrangement and have strong fluid-structure coupling effects under excitations, so the calculation is computationally intensive. For such complex structures, the use of porous media models is an important means of structure simplification. The parameters of porous media are often calculated by CFD modeling, and the calculation process is complicated and time-consuming. BP neural network has strong nonlinear mapping capability and can be used to calculate the parameters of porous media. For different racks designs, the gap arrangement is different, and the fluid-structure coupling parameters are also different. Therefore, it is necessary to study the fluid-structure coupling parameters of square tube bundles such as racks. Taking porous storage racks as an example, by building different CFD models, 1366 sets of valid data were obtained for training. This paper uses BP neural network to study the porous medium parameters required for fluid-structure interaction of porous racks. Compared with the CFD calculation method of fine modeling, the calculation error of the additional mass of the porous media model established by the porous media parameters predicted by the neural network is controlled at about 10%. The research results provide a reference for the fast calculation of porous media parameters and fluid-structure interaction.
A Physics-Driven X-Ray Image Data Augmentation Method for Automated Threat Detection in Nuclear Facility Entrancement
Authors: Xu Shuo , Chen Gang , Li Weiwei , Xiang Xincheng 
Published: November 2022

Abstract
Due to the perspective ability of the internal structure of objects, the X-ray security screening system has been used to ensure nuclear security in nuclear facility entrancement. To improve detection efficiency and accuracy, recent work posed a particular interest in automated threat detection algorithms based on deep learning. A significant obstacle to developing high performance of such algorithms is the difficulty of obtaining large labeled training datasets. X-ray image data augmentation strategies based on experimental images have been proposed by other investigators. This paper proposed a physics-driven X-ray image data augmentation method for automated threat detection. Using the 3D modeling software, we can construct threat models and project them into threat images without experiment. According to Lambert-Beer law, the simulation image datasets can be made by exponentially overlaying the threat images on the background images. Considering the energy spectrum and scattering effect, we further process the phantoms and projection images accordingly to be more authentic. We train YOLOv5 architecture on the simulation dataset and test the algorithm on the experimental images. The results show that our approach achieves good performances in automated threat detection with an average recognition accuracy of over 90% and a mAP@0.5 of 82.9%, which is an effective method to increase the X-ray image dataset in both quantity and diversity.
Development of Adaptive Sampling Software Based on Relap5
Authors: Chen Haoyin , Wang He , Suliman Mohamedelmogtabh Omer Elfadni , Wang Xinyue , Zhao Qiang 
Published: November 2022

Abstract
The RISMC analysis method is a coupled dynamic safety analysis method that combines deterministic safety analysis and probabilistic safety analysis. It comprehensively simulates the dynamic response process of nuclear power plants under accidents through Monte Carlo sampling and dynamic event tree methods. In order to fully reflect the response process of the nuclear power plant and ensure the accuracy of the calculation results, it is necessary to sample a large number of input space parameters, which will result in unbearable time costs. Adaptive sampling can predict the state of the position input space parameters through a small number of forward sampling calculation results and divide the input space by generating limit surfaces to calculate the failure probability, which can greatly save computing time. Based on researching RISMC analysis tools at home and abroad, the research and development of adaptive sampling and RELAP5 coupling calculation software is realized. The test case of the Qinshan nuclear power plant station blackout accident shows that the adaptive sampling function of calculating failure space probability through a small amount of sampling is realized, and the efficiency of RISMC analysis and calculation is improved.
Simulation on the Process of Core Molten Pool Formation and Corium Relocation in the Core Region
Authors: Zhao Xinhai , Chen Peng , He Dongyu , Zhong Ruhao 
Published: November 2022

Abstract
Accurately estimating the moment on which the core melt relocates to the lower head in severe accidents is crucial for evaluating the effectiveness of the In-Vessel Retention (IVR) strategy. However, the collapse and fragmentation of the fuel rods as well as the melting and re-solidification of the core material during the core degradation process lead to complex changes in the core geometry, making it difficult to accurately simulate the relocation process of the melt in the core. Based on TMI-2 and Phebus FPT series experiments, a porous media model with transient flow and heat transfer both in the rod bundle region and debris bed region was established in this paper. The VOF and LEVEL SET methods were used to track the movement front of the corium in the core, as well as the coalescence process of local separate molten pools to form a large-scale core molten pool. Moreover, the efficient and stable IDEAL segregated algorithm was adopted to deal with the coupling problem of flow and heat transfer. The VOF and LEVEL SET methods were verified by the dam break problem, and the deviation of the results is within 10%. Using the above methods, the formation of a large-scale molten pool in the core and the relocation process of corium in the core region were simulated.