• Nuclear Engineering and Technology
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• v.41 no.9
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• pp.1181-1190
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• 2009

Knowing more about the Local Power Density (LPD) at the hottest part of a nuclear reactor core can provide more important information than knowledge of the LPD at any other position. The LPD at the hottest part needs to be estimated accurately in order to prevent the fuel rod from melting in a nuclear reactor. Support Vector Machines (SVMs) have successfully been applied in classification and regression problems. Therefore, in this paper, the power peaking factor, which is defined as the highest LPD to the average power density in a reactor core, was estimated by SVMs which use numerous measured signals of the reactor coolant system. The SVM models were developed by using a training data set and validated by an independent test data set. The SVM models' uncertainty was analyzed by using 100 sampled training data sets and verification data sets. The prediction intervals were very small, which means that the predicted values were very accurate. The predicted values were then applied to the first fuel cycle of the Yonggwang Nuclear Power Plant Unit 3. The root mean squared error was approximately 0.15%, which is accurate enough for use in LPD monitoring and for core protection that uses LPD estimation.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2162-2173
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• 2021

In this paper, the coupling of fine-mesh computational fluid dynamics (CFD) thermal-hydraulics (TH) code and neutronics code is achieved using the Ansys Fluent User Defined Function (UDF) for code development, including parallel meshing mapping, data computation, and data transfer. Also, some CFD schemes are designed for mesh mapping and data transfer to guarantee physical conservation in the coupling computation. Because there is no rigorous research that gives robust guidance on the various CFD schemes that must be obtained before the fine-mesh coupling computation, this work presents a quantitative analysis of the CFD meshing and mapping schemes to improve the accuracy of the value and location of key physical prediction. Furthermore, the effect of the sub-pin scale coupling computation is also studied. It is observed that even the pin-resolved coupling computation can also create a large deviation in the maximum value and spatial locations, which also proves the significance of the research on mesh mapping and data transfer for CFD code in a coupling computation.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.193-206
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• 2022

Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks' sizes.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.444-449
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• 2008

Fire model shall be verified and validated to reliably predict the consequences of fires within its limitations. This study aims to predict pool fire phenomena with two rooms using CFAST and to compare CFAST simulation results with PRISME experimental data which can be applicable to the fire of nuclear power plant facility. Five different mass loss rate(MLR) are used in the simulation and the simulated results of specific quantities such as temperature, chemical composition are compared to the experimental data. From this study, the CFAST simulation results with the proper MLR show better similarity and trend with pool fire experimental data.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3260-3271
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• 2022

Experience shows that the solution to waste management in any national programme is lengthy and burdened with uncertainties. There are several uncertainties that contribute to the costs associated with spent fuel management. In this work, we have analysed the impact of the current nuclear data on the isotopic composition of the spent fuel and consequently their influence on the main spent fuel observables such as decay heat, activity, neutron multiplication factor, and neutron and photon source terms. Nuclear libraries based on the most general nuclear data ENDF/B-VII.0, ENDF/B-VII.1, ENDF/B-VIII.0 and JEFF-3.3 are considered. A typical NPP Krško fuel assembly is analysed using the Monte Carlo code Serpent 2. The analysis considers burnup of up to 60 GWd/tU and cooling times of up to 100 years. The comparison of results showed significant differences, which should be taken into account when selecting the library and evaluating the uncertainty in determining the characteristics of the spent fuel.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4189-4200
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• 2021

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2725-2732
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• 2020

This paper presents the neutronics benchmark analysis of the first core of the Indonesian multipurpose research reactor RSG-GAS (Reaktor Serba Guna G.A. Siwabessy) calculated by the Serpent Monte Carlo code and the newly released ENDF/B-VIII.0 nuclear data library. RSG-GAS is a 30 MWth pool-type material testing research reactor loaded with plate-type low-enriched uranium fuel using light water as a coolant and moderator and beryllium as a reflector. Two groups of critical benchmark problems are derived on the basis of the criticality and control rod calibration experiments of the first core of RSG-GAS. The calculated results, such as the neutron effective multiplication factor (k) value and the control rod worth are compared with the experimental data. Moreover, additional calculated results, including the neutron spectra in the core, fission rate distribution, burnup calculation, sensitivity coefficients, and kinetics parameters of the first core will be compared with the previous nuclear data libraries (interlibrary comparison) such as ENDF/B-VII.1 and JENDL-4.0. The C/E values of ENDF/B-VIII.0 tend to be slightly higher compared with other nuclear data libraries. Furthermore, the neutron reaction cross-sections of ¹⁶O, ⁹Be, ²³⁵U, ²³⁸U, and S(𝛼,𝛽) of ¹H in H₂O from ENDF/B-VIII.0 have substantial updates; hence, the k sensitivities against these cross-section changes are relatively higher than other isotopes in RSG-GAS. Other important neutronics parameters such as kinetics parameters, control rod worth, and fission rate distribution are similar and consistent among the nuclear data libraries.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3073-3084
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• 2022

The propagation of radiation source uncertainties in spent nuclear fuel (SNF) cask shielding calculations is presented in this paper. The uncertainty propagation employs the depletion and source term outputs of the deterministic code STREAM as input to the transport simulation of the Monte Carlo (MC) codes MCS and MCNP6. The uncertainties of dose rate coming from two sources: nuclear data and modeling parameters, are quantified. The nuclear data uncertainties are obtained from the stochastic sampling of the cross-section covariance and perturbed fission product yields. Uncertainties induced by perturbed modeling parameters consider the design parameters and operating conditions. Uncertainties coming from the two sources result in perturbed depleted nuclide inventories and radiation source terms which are then propagated to the dose rate on the cask surface. The uncertainty analysis results show that the neutron and secondary photon dose have uncertainties which are dominated by the cross section and modeling parameters, while the fission yields have relatively insignificant effect. Besides, the primary photon dose is mostly influenced by the fission yield and modeling parameters, while the cross-section data have a relatively negligible effect. Moreover, the neutron, secondary photon, and primary photon dose can have uncertainties up to about 13%, 14%, and 6%, respectively.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.125-125
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• 1998