• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.3
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• pp.200-205
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• 2000

In a previous paper, we have discussed the intelligent Windows 95-based data management program(IDPIN) which was developed for effective and efficient management of large amounts of pre-/in-service inspection(PSI/ISI) data of Kori nuclear power plants. The IDPIN program enables the prompt extraction of previously conducted PSI/ISI conditions and results so that the time-consuming data management, painstaking data processing and analysis of the past are avoided. In this study, the intelligent Windows based data management program(WS-IDPIN) has been developed as an effective data management of PSI/ISI data for the Wolsong nuclear power plants. The WS-IDPIN program includes the modules of comprehensive management and analysis of PSI/ISI results, statistical reliability assessment program of PSI/ISI results(depth and length sizing performance etc), standardization of UT report form and computerization of UT results. In addition, the program can be further developed as a unique PSI/ISI data management expert system which can be part of the PSI/ISI total support system for Korean nuclear power plants.

• Nuclear Engineering and Technology
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• v.39 no.6
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• pp.747-752
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• 2007

The Proton Engineering Frontier Project (PEFP) has designed and developed a proton linear accelerator facility operating at 100 MeV - 20 mA. The radiological effects of such a nuclear facility on the environment are important in terms of radiation safety. This study estimated the production rates of radionuclides in the soil around the accelerator facility using MCNPX. The groundwater migration of the radioisotopes was also calculated using the Concentration Model. Several spallation reactions have occurred due to leaked neutrons, leading to the release of various radionuclides into the soil. The total activity of the induced radionuclides is approximately $2.98{\times}10^{-4}Bq/cm^3$ at the point of saturation. $^{45}Ca$ had the highest production rate with a specific activity of $1.78{\times}10^{-4}Bq/cm^3$ over the course of one year. $^3H$ and $^{22}Na$ are usually considered the most important radioisotopes at nuclear facilities. However, only a small amount of tritium was produced around this facility, as the energy of most neutrons is below the threshold of the predominant reactions for producing tritium: $^{16}O(n,\;X)^3H$ and $^{28}Si(n,X)^3H$ (approximately 20 MeV). The dose level of drinking water from $^{22}Na$ was $1.48{\times}10^{-5}$ pCi/ml/yr, which was less than the annual intake limit in the regulations.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4447-4464
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• 2023

This paper focuses on the development of a new computational model of the CNESTEN's TRIGA Mark II research reactor using the 3D continuous energy Monte-Carlo code TRIPOLI-4 (T4). This new model was developed to assess neutronic simulations and determine quantities of interest such as kinetic parameters of the reactor, control rods worth, power peaking factors and neutron flux distributions. This model is also a key tool used to accurately design new experiments in the TRIGA reactor, to analyze these experiments and to carry out sensitivity and uncertainty studies. The geometry and materials data, as part of the MCNP reference model, were used to build the T4 model. In this regard, the differences between the two models are mainly due to mathematical approaches of both codes. Indeed, the study presented in this article is divided into two parts: the first part deals with the development and the validation of the T4 model. The results obtained with the T4 model were compared to the existing MCNP reference model and to the experimental results from the Final Safety Analysis Report (FSAR). Different core configurations were investigated via simulations to test the computational model reliability in predicting the physical parameters of the reactor. As a fairly good agreement among the results was deduced, it seems reasonable to assume that the T4 model can accurately reproduce the MCNP calculated values. The second part of this study is devoted to the sensitivity and uncertainty (S/U) studies that were carried out to quantify the nuclear data uncertainty in the multiplication factor k_eff. For that purpose, the T4 model was used to calculate the sensitivity profiles of the k_eff to the nuclear data. The integrated-sensitivities were compared to the results obtained from the previous works that were carried out with MCNP and SCALE-6.2 simulation tools and differences of less than 5% were obtained for most of these quantities except for the C-graphite sensitivities. Moreover, the nuclear data uncertainties in the k_eff were derived using the COMAC-V2.1 covariance matrices library and the calculated sensitivities. The results have shown that the total nuclear data uncertainty in the k_eff is around 585 pcm using the COMAC-V2.1. This study also demonstrates that the contribution of zirconium isotopes to the nuclear data uncertainty in the k_eff is not negligible and should be taken into account when performing S/U analysis.

• Nuclear Engineering and Technology
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• v.38 no.8
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• pp.803-808
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• 2006

The neutron induced nuclear cross section data for Ir-191 and Ir-193 were calculated and evaluated from unresolved resonance energy to 20MeV. The energy-dependent optical model potential parameters were determined based on the experimental data and applied up to 20MeV. A spherical optical model, a statistical model in an equilibrium energy region, and a multistep direct and multistep compound model in a pre-equilibrium energy region were used in the calculations. The direct capture model enhanced the fast neutron capture in the pre-equilibrium energy. The theoretically calculated cross sections were compared with the experimental data and the evaluated files. The calculations were found to be in good agreement with the experiment data. The evaluated cross section results were compiled with the ENDF-6 format. The fast energy results will be merged with the resonance parts to create a full evaluation library. The improvement of the neutron-induced cross section data will contribute to an increase in the efficiency of the production of Ir-192 as a radiation source.

• Nuclear Engineering and Technology
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• v.37 no.1
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• pp.101-108
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• 2005

The neutron induced nuclear cross-section data for Pd-105, Ag-109, Xe-131, and Cs-133 were calculated and evaluated from an unresolved energy to 20 MeV. The energy dependent optical model potential parameters were extracted based on recent experimental data and applied up to 20 MeV. A spherical optical model and a statistical model for the equilibrium energy, and a multistep direct and a multistep compound model for the pre-equilibrium energy were used in the calculation. The direct capture model was recently introduced for fast neutron capture. The theoretically calculated cross-sections were compared with the experimental data and the evaluated files. The total and capture cross-sections calculated using the model were in good agreement with the reference experimental data. The evaluated cross-section results were compiled in ENDF-6 format and merged with the resonance component, already adopted in the ENDF/B-VI release 8. New data library files covering from thermal to 20 MeV were created. They are at the preliminary stage of an ENDF/B- VII release.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.2
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• pp.102-108
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• 2003

This paper presents a new Data acquisition system that can improve a accuracy and response characteristics by designing a SDAS(Signal Datat Acquisition System). I&C(Instrumentation and Control) system, which it applied for Nuclear Power Plant, affects for safety either directly or indirectly. It should be assessed by the equipment qualification procedure to confirm the functionality during design life before it apply on the nuclear power plant depending on safety classification. This paper proposes data acquisition system that required as an essential measurement equipment in the method and procedure of equipment qualification following of standards and codes of IEEE and Nuclear Regulatory Guide. It provides comparable and experimental studies have been carried out. The presented results from the above investigation show considerably improved accuracy performance in the data acquisition system. This circuit will be able to be used in high performance I&C system.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2220-2238
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• 2024

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants.