bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of Sensitivity Analysis Techniques to a Low Dimensional Problem in the Frame of Severe Accidents
Authors: Angelucci Michela , Paci Sandro , Cancemi Salvatore Angelo , Lo Frano Rosa 
Published: November 2024

Abstract
The quantification of uncertainties in Severe Accident (SA) simulations is crucial for ensuring the robustness and enhancing the reliability of nuclear reactor safety assessments. However, traditional UQ methods often lack the ability to discern the individual contributions of various input parameters to the overall uncertainty in simulation results. To this end, a sensitivity analysis complementary to the uncertainty one seems indispensable.
In this framework, the present paper presents a twofold approach to address this challenge: on one side, by applying the so called single variable approach, in which the relationship between the selected output response and each input parameter taken individually is studied; on the other side, by integrating supervised Machine Learning (ML) techniques into the sensitivity analysis of SA simulations.
The selected sensitivity analysis techniques are applied to the outcomes of SA simulations conducted as part of the recently concluded “Management and Uncertainty of Severe Accidents” (MUSA) EURATOM project. In particular, results from a training exercise, involving an exploratory UQ application with a simplified SA scenario (namely the Phebus FPT1 test) and a limited number of input parameters, are employed as a basis for the performed sensitivity analysis.
As a result of the application of the different sensitivity methods, the parameters, or “features”, selected by each one of them are reported and documented. Additionally, a thorough cross-comparison of the obtained results is conducted in the attempt to work out an optimization of the sensitivity analysis process.
Study on Radionuclide Desorption in Matrix Graphite Under Accident Conditions
Authors: Xu Wei , Li Jian 
Published: November 2024

Abstract
High temperature gas cooled reactor (HTGR) is one of the promising advanced nuclear reactor for future electricity generation. The spherical fuel element with thousands of TRISO-coated particles dispersed in the graphite matrix are adopted by the HTGR, which is an important guarantee for the inherent safety of this type of reactor. The TRISO fuel particle typically consists of a very small fuel kernel in the central region, and surrounded by four coating layers. These coating layers constitute the principal containment barrier for radioactivity release under normal and accident conditions. In the spherical fuel element, the radionuclide release is dominated by multiple physical and chemical processes, such as fission product transport, recoil, adsorption, desorption, and etc. The matrix graphite would be oxidized by water or air under water ingress or air ingress accident, which causing an increase of the porosity of graphite. The increase of graphite porosity affects the diffusion and adsorption of radionuclides in the matrix graphite, thereby impacting the release of radioactive nuclides. This paper studies the adsorption process of radionuclides and analyzes the influence of graphite oxidation on the adsorption rate under water ingress and air ingress accident. The adsorption rate of several safety related radionuclides (such as 131I, 110Ag, 137Cs, 90Sr) in the matrix graphite were analyzed in detail. The influence of temperature, adsorption energy and density of radionuclide in the matrix graphite on the adsorption rate is discussed. Among these relevant factors, temperature has the most significant influence. The density of radionuclide in the matrix graphite increases with the increase of graphite porosity and surface area due to graphite oxidation. Although the adsorption rate of nuclides in the matrix graphite will increase due to oxidation, the adsorption rate of nuclides is almost zero under water ingress and air ingress accident, and the increase of the adsorption rate caused by oxidation can be negligible.
Study on NUREG-2215 Regulatory Condition for Reflooding of the Previously Dried High Burnup Fuel Is Not Allowable During Dry Storage
Authors: Liao Ting-Yi , Tseng Yi-Fan , Lan Kuan-Che , Chiu Wan-June , Tung Hsiao-Ming 
Published: November 2024

Abstract
This paper studies the regulatory condition for reflooding of the previously dried high burnup fuel is not allowable in the NUREG-2215 review guidance issued by the NRC, to evaluate the regulatory applicability of this condition to the retrieval operations in Taiwan’s indoor dry storage facilities.
The discussion content firstly explains the research background and purpose of this paper, and then introduces NUREG-2215. And refer to NUREG CR-7198, Revision 1, NUREG-2224 and other research reports, as well as NRC’s response to public comments, to discuss the regulatory basis for reflooding of the previously dried high burnup fuel is not allowable. And from the NUREG-2224 evaluation results, it is known that intact high burnup fuel can withstand at least one thermal cycle exceeding 65°C without affecting the safety analysis of transportation or storage based on design basis accidents. The latest NRC review operation has also eliminated the restriction of reflooding, but high burnup fuel stored for more than 20 years needs to undergo additional supplementary analysis according to the recommended method.
Subsequently, the feasibility of applying this regulatory condition to Taiwan is evaluated based on items such as the regulatory situation, characteristics of spent nuclear fuel cladding, retrieval facilities, and retrieval operations.
A Benchmark Test of CHF Test Facility in CIAE to Columbia University HTRF
Authors: Duan Minghui , Xu Yongwang , Zhang Dongxu , Zhao Minfu , Wei Junhan , Yang Bing , Li Qingyuan , Wang Wei 
Published: November 2024

Abstract
Critical heat flux (CHF) is one of the most concerned thermal hydraulic phenomena in reactor design and safety analysis. As CHF involves complex two-phase flow heat transfer mechanism, and hasn’t been fully understood, the prediction of CHF mainly relies on CHF correlations obtained under limited experimental conditions. At present, CHF tests of a new type of PWR fuel assembly are being carried out on the Thermal-hydraulic Comprehensive Test Facility (TCTF) in China Institute of Atomic Energy (CIAE). In order to verify the reliability of the facility, testing method and measurements, a benchmark test has been performed on TCTF, which repeated the CHF test performed on the Heat Transfer Research Facility (HTRF) at Columbia University in the United States. The test section on TCTF was a 5 × 5 uniformly-heated rod bundle with spacer grids, and its sizes were the same as prototype. The range of test conditions covered the local pressure of 7.4∼16.8 MPa, the inlet mass flux of 1.0∼4.0 t/m2s, and the inlet temperature of 120∼266 °C. Verifications including the redundant measurements comparison, the repeatability of test data under the same thermal hydraulic parameters, the thermal balance verification and linearity of data were carried out to prove the reliability of the test data. And CHF test data comparison between TCTF test and HTRF test has been carried out. The comparation results showed that the deviation of the CHF values in TCTF test from HTRF test was within 6.6%, which indicated that the CHF tests in HTRF have been well repeated on TCTF. The benchmark test verified the accuracy and reliability of TCTF, testing methods and measurements, and TCTF can perform CHF tests to obtain reliable CHF data to evaluate the thermal-hydraulics performance of new PWR fuel assemblies.
Research on Pulse Discrimination for Alpha and Beta Measurement Based on Auto-Encoder
Authors: He Shuijun , Tang Lijuan , Liang Manchun , Li Ke , Wang Jihong , Du Xiaochuang 
Published: November 2024

Abstract
Pulse shape discrimination is an important method for measuring both gross alpha and gross beta in a single measurement. When the quenching level of the sample changes significantly, the percent spillover of the alpha events into the beta count and the beta events into the alpha count will change greatly. In order to ensure the accuracy of the measurement results, it is necessary to re-optimize the pulse shape discrimination parameter. For each sample measurement, a spiked sample with the same quenching level needs to be established for pulse shape discrimination parameter optimization. In this paper, we proposed a pulse discrimination method for alpha and beta decay based on auto-encoder, which only needs to be optimized once, and can adapt to the significant changes in the quenching level of the sample. The pulse shape discrimination model was trained on the measurement data of Am241 spiked samples with different quenching levels, and verified by the measurement data of Sr90 spiked samples with different quenching levels. Finally, the optimal parameter for pulse shape discrimination was determined. The model was tested using the measurement data of Am241 and Sr90 spiked samples with different quenching levels. These samples quenching level range from 306 to 433 which represented by transformed spectral index of the external standard. The percentage of alpha pulse misclassification is 2.21%–3.35%, and the percentage of beta pulse misclassification is 2.37%–2.82%.
Enhancing Nuclear Power Plant Operational Forecasting With Transformer Neural Networks: a Time-Series Data Approach
Authors: Tuo Yanjie , Liu Xiaojing 
Published: November 2024

Abstract
The forecasting of critical operational parameters within nuclear power plant is paramount for ensuring their operational safety and efficiency. In the realm of predictive analytics, the Transformer neural network architecture has distinguished itself by delivering superior performance across a diverse array of fields. Despite its proven efficacy, the exploration of Transformer models within the nuclear energy sector is notably sparse. This investigation seeks to bridge this gap by deploying Transformer-based predictive methodologies on a dataset characterizing the loss of offsite power scenario at a nuclear power plant. Through the application of the Tree-structured Parzen Estimator optimization algorithm, the study meticulously optimizes the neural network architecture to refine its predictive precision. The research effectively forecasts critical physical metrics such as core pressure vessel water level, core outlet temperature (average), hotleg temperature, coldleg temperature, steam line pressure, steam generator secondary feedwater flow rate. The empirical results of this study are promising, demonstrating the Transformer model’s good capability in predictions, with the normalized Root Mean Square Error for all evaluated physical parameters on the test dataset consistently remaining below 0.056. This outcome underscores the Transformer’s potential as a robust tool for predictive analytics in nuclear power plant operations.
Effect of Temperature and Lithium Concentration on the Out of Reactor Corrosion Property of SZA Alloys and Model Study
Authors: Zeng Qifeng , Wang Chen , Wang Ben , Lu Junqiang , Li Cong , Zhang Lefu , Si Shengyi 
Published: November 2024

Abstract
Zirconium alloys have been widely used as fuel cladding and structural materials for pressurized water reactor (PWR) fuel assemblies because of their stable dimensional and physical properties under neutron irradiation environment, low thermal neutron absorption cross section, good corrosion resistance and mechanical properties at operating temperature. During the operation of zirconium alloy cladding, corrosion resistance is a critical life-limiting degradation mechanism, temperature and lithium concentration are the key factors for corrosion resistance. In order to study the effects of temperature and lithium concentration on the corrosion resistance, the microstructural analysis of two new zirconium alloys named SZA and static autoclave corrosion tests under various conditions were carried out. The microstructure of two SZA alloys were analyzed by scanning electron microscope (SEM) and transmission electron microscope (TEM), including the microstructure of the zirconium alloys matrix, the size, distribution and structure of the second phase particles (SPPs). The corrosion weight gain curves of SZA alloys and Zr-4 alloy under different temperatures and lithium concentrations in high temperature and high pressure water were obtained through out of reactor static autoclave tests. The corrosion rate-temperature and corrosion rate-lithium concentration models of SZA alloys were obtained through data analysis and model fitting.
The experimental results show that the two SZA alloys are both partially recrystallized structure, and a large number of fine SPPs are dispersed in the zirconium alloys matrix. The average diameters of the SPPs in SZA-4 and SZA-6 alloys are 98.6nm and 100.7nm, respectively. There are mainly two kinds of SPPs containing trace Ge and small amount of Nb in SZA-4 alloy, including Zr(Fe,Nb,Cr,Ge)2 with face centered cubic (FCC) crystal structure and Zr(Fe,Nb,Cr,Ge)2 with hexagonal close packed (HCP) crystal structure. There are three kinds of SPPs containing trace Cr in SZA-6 alloy, including a large amount of Zr(Fe,Nb,Cr)2 with HCP crystal structure and Zr2(Fe,Nb,Cr) with FCC crystal structure, and a small amount of Zr(Fe,Nb,Cr)2 with FCC crystal structure. SZA alloys have better corrosion resistance than Zr-4 alloy under a variety of temperature and lithium concentration test conditions, except that there is no obvious difference in the corrosion resistance of the three alloys at 315 °C due to the low test temperature. At the same lithium concentration, increased corrosion rate of the three zirconium alloys were observed with an increase in test temperature. At the same temperature, increased corrosion rate of the three zirconium alloys were observed with an increase in lithium concentration. The model fitting results indicate that the corrosion rate of SZA alloys are exponentially related to temperature, and a quadratic or linear relationship with lithium concentration.
Improving the Accuracy of RCCS Simulator Based on Ensemble Kalman Filter Algorithm
Authors: Liu Songsong , Zhang Han , Wu Yingjie , Lang Minggang , Dong Yujie , Li Fu 
Published: November 2024

Abstract
Data assimilation, an effective approach that utilizes measured data and suitable assimilation algorithms to reduce existing model calculation errors, has its origins in weather forecasting research. In this study, a framework of data assimilation based on Kalman filter (EnKF) algorithm is established for the computational program of the reactor cavity cooling system (RCCS), an important component of high temperature gas-cooled reactor-pebble-bed module (HTR-PM). Original model with uncertain parameters and conservative assumptions gives a result deviating from the measured data. After assimilation, parameters including the thermal conductivity of the water cooling wall, blackness coefficient, material reflectivity, resistance coefficients of various components, and the frontal area of the air cooler, are recalibrated. The modified model significantly improves the accuracy of predicting the residual heat removal power, reducing the calculation error from 16.89% to 1.22% compared to the original model. By applying the EnKF algorithm, this paper expands its application scope within nuclear engineering, providing inspiration for future relative studies.
Optimal Sensor Placement in Nuclear Power Steam Water System Based on Signed Directed Graph
Authors: Xing Tianyang , Zeng Chunyang , Han Bin , Jiang Mudi , He Yunze , Liu Shenghui , Huang Junling , Zhu Xiaoliang 
Published: November 2024

Abstract
Nuclear power plant is expected to achieve the comprehensive fault monitoring and ensure the secure and stable operation. Enhancing the reliability of the digital measurement system will effectively mitigate the potential risks associated with compromised operation of the nuclear power system, confine the extent of fault impact, and bolster the operator’s situational awareness proficiency. This paper presents a comprehensive modeling strategy for obtaining the fault-sensor correlation matrix in the deaerator system of a nuclear power plant based on SDG theory. Based on the utilization of Advanced Process Simulation Software (APROS), we successfully developed a dynamic numerical simulation model for the deaeration system in nuclear power plants. By conducting a comprehensive analysis of the correlation between fault propagation path and sensor, we have successfully established a probability model that accurately assesses the performance of sensor fault detection. A multi-objective optimization model for sensor layout, considering the cost of sensor placement, detection ability, and fault correlation, was developed based on constraint relationships. Compared to conventional sensor placement schemes, the optimized sensor placement scheme exhibits superior performance in terms of cost-effectiveness in arranging sensors, reliability in fault detection, and correlation between faults and sensor points.
Investigation on Oxidation Behaviors of FeCrAl Alloy Cladding Under the Simulated LOCA Conditions
Authors: Chen Kang , Duan Zhengang , Wen Qinglong , Zhang Ruiqian 
Published: November 2024

Abstract
During a LOCA (Loss of Coolant Accident), the zirconium alloy cladding undergoes oxidation, generating significant heat and hydrogen gas. In extreme circumstances, this can result in hydrogen explosions compromising reactor safety. Recently, the ATF (Accident Tolerant Fuel) cladding has been introduced as a new generation of fuel cladding. Among the ATF claddings, FeCrAl alloy cladding exhibits a low oxidation rate during LOCA due to the formation of a dense alumina layer during high-temperature oxidation. However, current research on this FeCrAl alloy cladding remains limited in scope and depth. The present study focuses on the oxidation behavior of FeCrAl alloy cladding at 1200 °C in steam. Key findings include: (1) Spots on the sample were identified as oxides of Fe, Cr, and Al through microscopic analysis. (2) An oxide layer of Al was observed with a thickness ranging from 0.86 μm to 1.98 μm. (3) The ring compression test revealed that the FeCrAl alloy cladding neither fractured nor underwent a ductile-brittle transition.
Suppression of Harmonic Vibration in AMB System Using Nonlinear Adaptive Resonant Controllers
Authors: Bian Xiaoyu , Shi Zhengang , Mo Ni , Sun Zhe 
Published: November 2024

Abstract
The primary helium circulator (PHC) serves as the core of the high-temperature gas-cooled reactor. Due to the non-contact nature of active magnetic bearing (AMB), it is employed as the supporting apparatus for the rotating machinery of the PHC to ensure the purity of the helium environment within the high-temperature gas-cooled reactor. Nevertheless, the rotor mass imbalance and sensor runout may lead to synchronous vibrations and harmonic vibrations within the AMB system. These vibrations are subsequently transmitted to the shell, resulting in foundational vibrations that pose a significant threat to the stability and safety of the system. This paper introduces a novel nonlinear adaptive resonance controller to suppress both synchronous vibrations and harmonic vibrations in the system. The algorithm features a straightforward structure with minimal computational complexity and operates without reliance on the mathematical model of the system. The algorithm proposed in this paper consists of three modules: the rotor phase calculation module, the resonance signal extraction module, and the adaptive iteration module. The rotational speed sensor is utilized by the rotor phase calculation module to calculate the rotor phase within one rotation period. The resonance signal extraction module can calculate the synchronous and harmonic energies within one iteration period. The adaptive iteration module can output the feed-forward compensation signals with proper amplitudes and phases. Finally, the simulation results validated the effectiveness of the algorithm.
A Control-Oriented Hybrid Model for Nuclear Reactors Based on Neural Network
Authors: Zhu Ze , Liang Wenlong , Ye Baiqing , Jiang Qingfeng , Wang Pengfei 
Published: November 2024

Abstract
Accurate modeling is the basis for analyzing the dynamic response characteristics of the model. However, due to the complexity and time-varying nature of the internal mechanisms of the reactor, it is inevitable that inaccurate model parameters will be used in the modeling process. These will lead to discrepancies between the modeled mechanisms and the actual reactor. In this paper, the difference is evaluated and shortened by means of neural network hybrid modeling. Based on the MATLAB/Simulink simulation platform, this paper firstly obtains the parameters that have the greatest influence on the linear model through sensitivity analysis and takes them as the object of neural network correction, then obtains the data required for offline training of neural network according to the mechanism model, linear model and the deviation of the two under different working condition levels, retains the neural network weights and thresholds obtained from the offline training, and finally utilizes the gradient descent algorithm to update the neural network weights and thresholds in real time in order to achieve the online calibration of the linear model. The final results show that the hybrid model can effectively reduce the steady-state deviation between the two models, which indicates that the hybrid modeling can effectively improve the accuracy of the established model and provide a solid foundation for the subsequent design of the control system based on the linear model.
Study on the Influence of Internal Heat Source Characteristics and Distribution on Effective Thermal Conductivity of Cylindrical Particle Dispersed Fuel
Authors: Liu Yuhao , Sun Jun 
Published: November 2024

Abstract
Dispersing fuel particles within a matrix is a pivotal strategy for next-generation nuclear fuels. The internal temperature distribution of dispersion fuels is generally determined by the lumped parameter method, where Effective Thermal Conductivity (ETC) plays a critical role. Most computations yet use classical ETC models that lack consideration for internal heat sources, such as Maxwell-Eucken model, Chiew-Glandt model, and Effective Medium Theory. Some researchers have noted that classical models tend to underestimate the temperature when internal heat sources exist. Additionally, the stochastic distribution of heat sources introduces uncertainty in ETC values and, consequently, temperature predictions. This study focuses on cylindrical dispersions with randomly distributed particles, and employs the three-dimensional finite element method to calculate their ETCs. Various parameters, including component thermal conductivity, particle radius, volume fraction, and minimum particle spacing, are adjusted in different cases. For each parameter configuration, a large number of cases are randomly generated and simulated. Statistical characteristics of ETC results are analyzed. It reveals that, when internal heat sources are considered, the value and uncertainty of cylindrical dispersion ETC are influenced not only by volume fraction and component thermal conductivity, but also by the size of heat-generating particles and the minimum particle spacing. It underscores the necessity of characterizing and quantifying the distribution of internal heat sources when proposing novel ETC models for dispersion fuel.