• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2258-2265
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• 2024

The present investigation aims to study the role of Pr₂O₃ on the structural, physical, and radiation shielding properties of a dense cadmium lead borate glass. The XRD was used to affirm the glassy amorphous structure of fabricated sample materials. Moreover, the FTIR was used to record the change in the FT-IR spectra due to the addition of Pr₂O₃ in the wavenumber interval between 400 and 4000 cm^-1. The features of glass surfaces and the elemental analyses for the synthesized Pr₂O₃-reinforced cadmium lead borate glasses were performed using a SEM, supported by an energy-dispersive spectrometer. The γ-ray protection capacity was evaluated using the Monte Carlo method in a wide energy interval ranging between 0.015 and 15 MeV. The linear attenuation coefficient (LAC) at 1 MeV was reduced by a factor of 10 % from 0.372 cm^-1 to 0.340 cm^-1. The decrease in the LAC values negatively affected the other shielding properties such as half-value thickness and the transmission factor. Although the linear attenuation coefficient is decreased slightly with the partial substitution of CdO by Pr₂O₃ compound, the fabricated glass samples still have a high shielding capacity compared to the traditional commercial glasses as well as previous similar reported glasses.

• Clean Technology
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• v.30 no.3
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• pp.248-257
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• 2024

Carbon dioxide is an undesired component of biogas and landfill gas. As a result, it needs to be removed from these mixtures in order to increase their heating value and reduce corrosion during treatment. Zeolites are a class of microporous materials that can be used as adsorbents for the separation of carbon dioxide from gas mixtures. In this work, the pure gas adsorption isotherms of methane and carbon dioxide and the selectivity of their mixture onto LTA-4A, FAU-13X and FAU-NaY adsorbents at temperatures of 273, 298 and 323 K and pressures up to 30 bars were calculated by the Monte Carlo method. Also, the influence of a flexible framework in a set of zeolites on the separation of methane and carbon dioxide was studied. Carbon dioxide adsorption onto the zeolites used in this work was more favorable than methane adsorption. The FAU-13X adsorbent had the highest adsorption capacity among the studied adsorbents. However, the selectivity of carbon dioxide over methane for LTA-4A was the highest. The adsorption capacities of a rigid framework were higher than those of a flexible framework. The influence of the framework flexibility in FAU on adsorption capacity was small. In contrast, its influence on selectivity seemed to be much larger.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.2
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• pp.77-84
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• 2021

A sampling-based approach was devised as a nuclear seismic probabilistic risk assessment (SPRA) method to account for the partially correlated relationships between components. However, since this method is based on sampling, there is a limitation that a large number of samples must be extracted to estimate the results accurately. Thus, in this study, we suggest an effective approach to improve the existing sampling method. The main features of this approach are as follows. In place of the existing Monte Carlo sampling (MCS) approach, the Latin hypercube sampling (LHS) method that enables effective sampling in multiple dimensions is introduced to the SPRA method. In addition, the degree of segmentation of the seismic intensity is determined with respect to the final seismic risk result. By applying the suggested approach to an actual nuclear power plant as an example, the accuracy of the results were observed to be almost similar to those of the existing method, but the efficiency was increased by a factor of two in terms of the total number of samples extracted. In addition, it was confirmed that the LHS-based method improves the accuracy of the solution in a small sampling region.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.217-224
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• 2024

This paper proposes an efficient dimension reduction method (DRM) that considers the nonlinearity of the performance functions in reliability-based design optimization (RBDO). The dimension reduction method evaluates the reliability more accurately than the first-order reliability method (FORM) using integration quadrature points and weights. However, its efficiency is hindered as the number of quadrature points increases owing to the need for an additional evaluation of the performance function. In this study, we assessed the nonlinearity of the performance function in RBDO and proposed criteria for determining the number of quadrature points based on the degree of nonlinearity. This approach suggests adjusting the number of quadrature points during each iteration of the RBDO process while maintaining the accuracy of theDRM while improving the computational efficiency. The nonlinearity of the performance function was evaluated using the angle between the vectors used in the maximum probable target point (MPTP) search. Numerical tests were conducted to determine the appropriate number of quadrature points according to the degree of nonlinearity. Through a 2D numerical example, it is confirmed that the proposed method improves the efficiency while maintaining the accuracy of the dimension reduction method or Monte Carlo Simulation (MCS).

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.613-619
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• 2005

Indigenous Yakutian cattle' adaptation to the hardest subarctic conditions makes them a valuable genetic resource for cattle breeding in the Siberian area. Since early last century, crossbreeding between native Yakutian cattle and imported Simmental and Kholmogory breeds has been widely adopted. In this study, variations at 22 polymorphic microsatellite loci in 5 populations of Yakutian, Kholmogory, Simmental, Yakutian-Kholmogory and Yakutian-Simmental cattle were analysed to estimate the genetic contribution of Yakutian cattle to the two hybrid populations. Three statistical approaches were used: the weighted least-squares (WLS) method which considers all allele frequencies; a recently developed implementation of a Markov chain Monte Carlo (MCMC) method called likelihood-based estimation of admixture (LEA); and a model-based Bayesian admixture analysis method (STRUCTURE). At population-level admixture analyses, the estimate based on the LEA was consistent with that obtained by the WLS method. Both methods showed that the genetic contribution of the indigenous Yakutian cattle in Yakutian-Kholmogory was small (9.6% by the LEA and 14.2% by the WLS method). In the Yakutian-Simmental population, the genetic contribution of the indigenous Yakutian cattle was considerably higher (62.8% by the LEA and 56.9% by the WLS method). Individual-level admixture analyses using STRUCTURE proved to be more informative than the multidimensional scaling analysis (MDSA) based on individual-based genetic distances. Of the 9 Yakutian-Simmental animals studied, 8 showed admixed origin, whereas of the 14 studied Yakutian-Kholmogory animals only 2 showed Yakutian ancestry (>5%). The mean posterior distributions of individual admixture coefficient (q) varied greatly among the samples in both hybrid populations. This study revealed a minor existing contribution of the Yakutian cattle in the Yakutian-Kholmogory hybrid population, but in the Yakutian-Simmental hybrid population, a major genetic contribution of the Yakutian cattle was seen. The results reflect the different crossbreeding patterns used in the development of the two hybrid populations. Additionally, molecular evidence for differences among individual admixture proportions was seen in both hybrid populations, resulting from the stochastic process in crossing over generations.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.105-115
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• 2008

Judgement post-stratification is a new sampling method developed by MacEachern et al. (2004). This article suggests that the judgement post-stratification method can be a good alternative for the simple random sampling when analyzing real-world environmental data. It becomes an important task to accurately measure the output of a recycling facility since the EPR (Extended Producer Responsibility) system takes effect on 2003. However, the total weight of materials processed in the recycling facility may not be a proper measure because the materials are frequently mingled with other non-recycling materials. Therefore, it is necessary to estimate the mixture ratio of non-recycling materials among the total materials admitted in the facility. Unfortunately, the size of sample in a recycling facility is restricted due to the inconvenience of sampling procedure such as safety, odor, time and classification of non-recycling materials. In this article, we showed the relative efficiency of the judgement post-stratification method over the simple random sampling method for equal sample sizes using Monte Carlo simulation. Furthermore, we applied the judgement post-stratification method on the 2004 recycling data and showed that it can replace the simple random sampling even with smaller observations.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2452-2459
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• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.125-130
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• 2011

Since conventional optimization that is classified as a deterministic method does not consider the uncertainty involved in a modeling or manufacturing process, an optimum design is often determined to be on the boundaries of the feasible region of constraints. Reliability-based design optimization is a method for obtaining a solution by minimizing the objective function while satisfying the reliability constraints. This method includes an optimization process and a reliability analysis that facilitates the quantization of the uncertainties related to design variables. Moment-based reliability analysis is a method for calculating the reliability of a system on the basis of statistical moments. In general, on the basis of these statistical moments, the Pearson system estimates seven types of distributions and determines the reliability of the system. However, it is technically difficult to practically consider the Pearson Type IV distribution. In this study, we propose an enhanced Pearson Type IV distribution based on a kriging model and validate the accuracy of the enhanced Pearson Type IV distribution by comparing it with a Monte Carlo simulation. Finally, reliability-based design optimization is performed for a system with type IV distribution by using the proposed method.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.3
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• pp.133-147
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• 2005

Various types of ray-tracing methods are used to predict the quantity measures of radiation illumination, the uniformity of illumination, radiation performance of LCD BLU(Hack-Light Unit). The uniformity of radiation illumination is one of the most important design factor of BLU and is usually controlled by the diffusive-ink pattern printed on the bottom of light-guide panel of BLU. Therefore it is desirable to produce an improved (ideally, the optimal) ink pattern to achieve the best uniformity of radiation illumination. In this paper, we applied the Welder-Mead simplex-search method among various direct search method to compute the optimal ink pattern. Direct search methods are widely used to optimize the functions which are often highly nonlinear, unpredictably discontinuous, and nondifferentiable, The ink-pattern controlling the uniformity of radiation illumination is one type of these functions. In this paper, we found that simplex search methods are well suited to computing the optimal diffusive-ink pattern. In extensive numerical testing, we have found the simplex search method to be reasonably efficient and reliable at computing the optimal diffusive-ink pattern. The result also suggests that optimization can improve the functionality of simulation tools which are used to design LCD BLU.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.207-216
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• 2014

Purpose : We present a method to reduce this gap and complete the treatment plan, to be made by the re-optimization is performed in the same conditions as the initial treatment plan different from Monaco treatment planning system. Materials and Methods : The optimization is carried in two steps when performing the inverse calculation for volumetric modulated radiation therapy or intensity modulated radiation therapy in Monaco treatment planning system. This study was the first plan with a complete optimization in two steps by performing all of the treatment plan, without changing the optimized condition from Step 1 to Step 2, a typical sequential optimization performed. At this time, the experiment was carried out with a pencil beam and Monte Carlo algorithm is applied In step 2. We compared initial plan and re-optimized plan with the same optimized conditions. And then evaluated the planning dose by measurement. When performing a re-optimization for the initial treatment plan, the second plan applied the step optimization. Results : When the common optimization again carried out in the same conditions in the initial treatment plan was completed, the result is not the same. From a comparison of the treatment planning system, similar to the dose-volume the histogram showed a similar trend, but exhibit different values that do not satisfy the conditions best optimized dose, dose homogeneity and dose limits. Also showed more than 20% different in comparison dosimetry. If different dose algorithms, this measure is not the same out. Conclusion : The process of performing a number of trial and error, and you get to the ultimate goal of treatment planning optimization process. If carried out to optimize the completion of the initial trust only the treatment plan, we could be made of another treatment plan. The similar treatment plan could not satisfy to optimization results. When you perform re-optimization process, you will need to apply the step optimized conditions, making sure the dose distribution through the optimization process.