• Nuclear Engineering and Technology
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• v.43 no.6
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• pp.573-582
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• 2011

The in-cabinet response spectrum is used to define the input motion in the seismic qualification of instruments and devices mounted inside an electrical cabinet. This paper presents a procedure for generating the in-cabinet response spectrum for electrical equipment based on in-situ testing by an impact hammer. The proposed procedure includes an algorithm to build the relationship between the impact forces and the measured acceleration responses of cabinet structures by estimating the state-space model. This model is used to predict seismic responses to the equivalent earthquake forces. Three types of structural model are analyzed for numerical verification of the proposed method. A comparison of predicted and simulated response spectra shows good convergence, demonstrating the potential of the proposed method to predict the response spectra for real cabinet structures using vibration tests. The presented procedure eliminates the uncertainty associated with constructing an analytical model of the electrical cabinet, which has complex mass distribution and stiffness.

• Nuclear Engineering and Technology
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• v.27 no.5
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• pp.743-752
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• 1995

This paper investigates the effects of fuel groupings in the coupled internals and core model on the internals and fuel responses due to pipe breaks. The 177 fuel assemblies for Korean standard nuclear power plant are grouped into several stick models and the responses of internals components are calculated. The analysis results show that the fuel model groupings in the coupled internals and core model have no significant effects on the internals and fuel responses for pipe break excitation. Also, in order to determine the feasibility of constructing a single equivalent stick representation of In or more adjacent fuel bundles, the reduced models, each of which employs a different stiffness lumping rule, are constructed. It is shown that the equivalent stiffness calculated to get the first natural frequency of the original model while preserving net gap between grouping centers gives the minimum modelling error.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2614-2624
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• 2022

Monte Carlo simulations were used to model a portable Neutron backscattering (NBT) sensor suitable for detecting plastic anti-personnel mines (APMs) buried in dry and moist soils. The model consists of a 100 MBq ²⁵²Cf source encapsulated in a neutron reflector/shield assembly and centered between two ³He detectors. Multi-parameter optimization was performed to investigate the efficiency of Be/Zr(BH₄)₄ and Be/Be(BH₄)₂ assemblies in terms of increasing the signal-to-background (S/B) ratio and reducing the total dose equivalent rate. The MCNP results showed that 2 cm Be/3 cm Zr(BH₄)₄ and 2 cm Be/3 cm Be(BH₄)₂ are the optimal configurations. However, due to portability requirements and abundance of Be, the ²⁵²Cf-2 cm Be/3 cm Be(BH₄)₂ NBT model was selected to scan the center of APM buried 3 cm deep in dry and moist soils. The selected NBT model has positively identified the APM with a S/B ratio of 886 for dry soils of 1 wt% hydrogen content and with S/B ratios of 615, 398, 86, and 12 for the moist soils containing 4, 6, 10, and 14 wt% hydrogen, respectively. The total dose equivalent rate reached 0.0031 mSv/h, suggesting a work load of 8 h/day for 806 days within the permissible annual dose limit of 20 mSv.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.9-19
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• 2015

The most important challenge in the region-based abstraction method as an approach to compute the state space of time Petri Nets (TPNs) for model checking is that the method results in a huge number of regions, causing a state explosion problem. Thus, region-based abstraction methods are not appropriate for use in developing practical tools. To address this limitation, this paper applies a modification to the basic region abstraction method to be used specially for computing the state space of TPN models, so that the number of regions becomes smaller than that of the situations in which the current methods are applied. The proposed approach is based on the special features of TPN that helps us to construct suitable and small region graphs that preserve the time properties of TPN. To achieve this, we use TPN-TCTL as a timed extension of CTL for specifying a subset of properties in TPN models. Then, for model checking TPN-TCTL properties on TPN models, CTL model checking is used on TPN models by translating TPN-TCTL to the equivalent CTL. Finally, we compare our proposed method with the current region-based abstraction methods proposed for TPN models in terms of the size of the resulting region graph.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.313-326
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• 2007

In this paper, an algorithm to reconstruct two orthogonal images into a three-dimensional image is developed in order to measure the bubble size and volume in a two-phase boiling flow. The central-active contour model originally proposed by P. $Szczypi\'{n}ski$ and P. Strumillo is modified to reduce the dependence on the initial reference point and to increase the contour stability. The modified model is then applied to the algorithm to extract the object boundary. This improved central contour model could be applied to obscure objects using a variable threshold value. The extracted boundaries from each image are merged into a three-dimensional image through the developed algorithm. It is shown that the object reconstructed using the developed algorithm is very similar or identical to the real object. Various values such as volume and surface area are calculated for the reconstructed images and the developed algorithm is qualitatively verified using real images from rubber clay experiments and quantitatively verified by simulation using imaginary images. Finally, the developed algorithm is applied to measure the size and volume of vapor bubbles condensing in a subcooled boiling flow.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1284-1295
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• 2024

Due to increasing operational security demands, digital and intelligent condition monitoring of nuclear power plants is becoming more significant. However, establishing an accurate and effective anomaly detection model is still challenging. This is mainly because of data characteristics of nuclear power data, including the lack of clear class labels combined with frequent interference from outliers and anomalies. In this paper, we introduce a Transformer-based unsupervised model for anomaly detection of nuclear power data, a modified loss function based on the maximum correntropy criterion (MCC) is applied in the model training to improve the robustness. Experimental results on simulation datasets demonstrate that the proposed Trans-MCC model achieves equivalent or superior detection performance to the baseline models, and the use of the MCC loss function is proven can obviously alleviate the negative effect of outliers and anomalies in the training procedure, the F1 score is improved by up to 0.31 compared to Trans-MSE on a specific dataset. Further studies on genuine nuclear power data have verified the model's capability to detect anomalies at an earlier stage, which is significant to condition monitoring.

• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.98-113
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• 2016

A reactor vessel level monitoring system measures the water level in a reactor during normal operation and abnormal conditions. A drop in the water level can expose nuclear fuel, which may lead to fuel meltdown and radiation spread in accident conditions. A level monitoring system mainly consists of a sensing line and pressure transmitter. Over a period of time boron sediments or other impurities can clog the line which may degrade the accuracy of the monitoring system. The aim of this study is to determine blockage in a sensing line using the energy of the composite signal. An equivalent Pi circuit model is used to simulate blockages in the sensing line and the system's response is examined under different blockage levels. Composite signals obtained from the model and plant's unblocked and blocked channels are decomposed into six levels of details and approximations using a wavelet filter bank. The percentage of energy is calculated at each level for approximations. It is observed that the percentage of energy reduces as the blockage level in the sensing line increases. The results of the model and operational data are well correlated. Thus, in our opinion variation in the energy levels of approximations can be used as an index to determine the presence and degree of blockage in a sensing line.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.13-30
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• 2019

This paper presents the recent improvements in the DeCART code for HTGR analysis. A new 190-group DeCART cross-section library based on ENDF/B-VII.0 was generated using the KAERI library processing system for HTGR. Two methods for the eigen-mode adjoint flux calculation were implemented. An azimuthal angle discretization method based on the Gaussian quadrature was implemented to reduce the error from the azimuthal angle discretization. A two-level parallelization using MPI and OpenMP was adopted for massive parallel computations. A quadratic depletion solver was implemented to reduce the error involved in the Gd depletion. A module to generate equivalent group constants was implemented for the nodal codes. The capabilities of the DeCART code were improved for geometry handling including an approximate treatment of a cylindrical outer boundary, an explicit border model, the R-G-B checker-board model, and a super-cell model for a hexagonal geometry. The newly improved and implemented functionalities were verified against various numerical benchmarks such as OECD/MHTGR-350 benchmark phase III problems, two-dimensional high temperature gas cooled reactor benchmark problems derived from the MHTGR-350 reference design, and numerical benchmark problems based on the compact nuclear power source experiment by comparing the DeCART solutions with the Monte-Carlo reference solutions obtained using the McCARD code.

• IE interfaces
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• v.24 no.3
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• pp.173-186
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• 2011

In this paper, we evaluate life annuity plans for Korean pre-retired single and married couple participating Korea National Pension (KNP) and find optimal life annuity strategy by using utility-based measurements called AEW (Annuity Equivalent Wealth). Specifically, we extend a previous study to obtain a detailed optimal combination of annuitizing age and wealth in terms of percentage of net wealth at the time of retirement. A nonlinear optimization model is formulated with the objective of maximizing utility on consumption and bequest, and the dynamic programming (DP) technique is used to solve this problem. We find that there exist consistent patterns in optimal combinations of annuitizing age and wealth. Also, for all cases the optimal combination is significantly better than several other combinations. The results indicate that using the optimal approach can be beneficial to practitioners in insurance industry and prospective purchasers of life annuity. We conclude the paper with some discussions and suggestions.

• Korean Journal of Agricultural Science
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• v.44 no.3
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• pp.424-439
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• 2017

This study was undertaken to examine the profit efficiency and its determining factors, the investment opportunity, and the challenges of shea butter producers in the northern region of Ghana. The methods employed in this research were the Stochastic Profit Frontier (SPF) model, gain-cost and investment return analyses, as well as Kendall's W statistic using primary data collected from 120 purposively-selected respondents. Results from the analysis indicated that profit efficiency was positively influenced by sex, household size, marital status, educational level, transportation cost, store rent, and price of shea nut with a gain in profit efficiency of 58.5%. The investment analysis demonstrated a net gain per person of $8,077 equivalent to GH₵ 28,270 Ghanaian cedi (GH₵) using 2016 exchange rate (GH₵ 3.5 = $1). Among the challenges identified, the poor quality of shea nuts was the most prioritised challenge with 72.8% agreement among the respondents. Based on these findings, it was recommended that proper training and education, as well as improvement in shea nut quality, should be promoted to improve the profit efficiency of shea butter producers.