• Nuclear Engineering and Technology
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• v.39 no.3
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• pp.207-214
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• 2007

A new Monte Carlo method for solving heat conduction problems is developed in this study. Differing from other Monte Carlo methods, it is a transport approximation to the heat diffusion process. The method is meshless and thus can treat problems with complicated geometry easily. To minimize the boundary effect, a scaling factor is introduced and its effect is analyzed. A set of problems, particularly the heat transfer in the fuel sphere of PBMR, is calculated by this method and the solutions are compared with those of an analytical approach.

• Journal of the Korean Society of Safety
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• v.18 no.1
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• pp.116-122
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• 2003

Monte Carlo method is one of the powerful engineering tools especially to solve the complex non-linear problems. The Monte Carlo method gives approximate solution to a variety of mathematical problems by performing statistical sampling experiments on a computer. One of the methods to predict the time dependent failure probability of one of the bridge components or the bridge system is a lifetime function. In this paper, FORTRAN program is developed to predict the failure probability of bridge components or bridge system by using both system reliability and lifetime function. Monte Carlo method is used to generate the parameters of the lifetime function. As a case study, the program is applied to the concrete-steel bridge to predict the failure probability.

• Nuclear Engineering and Technology
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• v.46 no.4
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• pp.481-488
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• 2014

An analysis of the uncertainty quantification related to LBLOCA using the Monte-Carlo calculation has been performed and compared with the tolerance level determined by the Wilks' formula. The uncertainty range and distribution of each input parameter associated with the LOCA phenomena were determined based on previous PIRT results and documentation during the BEMUSE project. Calulations were conducted on 3,500 cases within a 2-week CPU time on a 14-PC cluster system. The Monte-Carlo exercise shows that the 95% upper limit PCT value can be obtained well, with a 95% confidence level using the Wilks' formula, although we have to endure a 5% risk of PCT under-prediction. The results also show that the statistical fluctuation of the limit value using Wilks' first-order is as large as the uncertainty value itself. It is therefore desirable to increase the order of the Wilks' formula to be higher than the second-order to estimate the reliable safety margin of the design features. It is also shown that, with its ever increasing computational capability, the Monte-Carlo method is accessible for a nuclear power plant safety analysis within a realistic time frame.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1769-1780
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• 2022

A new method of dose calculation algorithm, called GPU-accelerated Monte Carlo and collapsed cone Convolution (GMCC) was developed to improve the calculation speed of BNCT treatment planning system. The GPU-accelerated Monte Carlo routine in GMCC is used to simulate the neutron transport over whole energy range and the Collapsed Cone Convolution method is to calculate the gamma dose. Other dose components due to alpha particles and protons, are calculated using the calculated neutron flux and reaction data. The mathematical principle and the algorithm architecture are introduced. The accuracy and performance of the GMCC were verified by comparing with the FLUKA results. A water phantom and a head CT voxel model were simulated. The neutron flux and the absorbed dose obtained by the GMCC were consistent well with the FLUKA results. In the case of head CT voxel model, the mean absolute percentage error for the neutron flux and the absorbed dose were 3.98% and 3.91%, respectively. The calculation speed of the absorbed dose by the GMCC was 56 times faster than the FLUKA code. It was verified that the GMCC could be a good candidate tool instead of the Monte Carlo method in the BNCT dose calculations.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4685-4694
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• 2023

The ability to calculate the material density sensitivity coefficients of power with respect to the material density has broad application prospects for accelerating Monte Carlo-Thermal Hydraulics iterations. The second-order material density sensitivity coefficients for the general Monte Carlo score have been derived based on the differential operator sampling method in this paper, and the calculation of the sensitivity coefficients of cell power scores with respect to the material density has been realized in continuous-energy Monte Carlo code RMC. Based on the power-density sensitivity coefficients, the sensitivity coefficients of power scores to some other physical quantities, such as power-boron concentration coefficients and power-temperature coefficients considering only the thermal expansion, were subsequently calculated. The effectiveness of the proposed method is demonstrated in the power-density coefficients problems of the pressurized water reactor (PWR) moderator and the heat pipe reactor (HPR) reflectors. The calculations were carried out using RMC and the ENDF/B-VII.1 neutron nuclear data. It is shown that the calculated sensitivity coefficients can be used to predict the power scores accurately over a wide range of boron concentration of the PWR moderator and a wide range of temperature of HPR reflectors.

• Journal of the Korean Chemical Society
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• v.62 no.3
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• pp.191-202
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• 2018

The aim of this work is to study Monte Carlo simulation of ethylene (co)polymerization over Ziegler-Natta catalyst as investigated by Chen et al. The results revealed that the Monte Carlo simulation was similar to sum square error (SSE) model to prediction of stage II and III of polymerization. In the case of activation stage (stage I) both model had slightly deviation from experimental results. The modeling results demonstrated that in homopolymerization, SSE was superior to predict polymerization rate in current stage while for copolymerization, Monte Carlo had preferable prediction. The Monte Carlo simulation approved the SSE results to determine role of each site in total polymerization rate and revealed that homopolymerization rate changed from site to site and order of center was different compared to copolymerization. The polymer yield was reduced by addition of hydrogen amount however there was no specific effect on uptake curve which was predicted by Monte Carlo simulation with good accuracy. In the case of copolymerization it was evolved that monomer chain length and monomer concentration influenced the rate of polymerization as rate of polymerization reduced from 1-hexene to 1-octene and increased when monomer concentration proliferate.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.8 no.2
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• pp.219-229
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• 1996

In the free molecular flow range, the pumping performance of a turbomolecular pump has been predicted by calculation of the transmission probability employing the integral method and the test particle Monte-Carlo method. The velocities of molecules incident upon a moving blade are given by the random numbers, which are sampled from the Maxwell molecular velocity distribution function. The present results agree quantitatively with the previous known numerical results. For a multi-stage pump, the velocity profile of molecules between two blade rows is not Maxwell distribution. In this case, the Monte-Carlo method is employed to calculate the overall transmission probability for the entire set of blade rows. When the results of the approximate method combining the single stage solutions are compared with those of the Monte-Carlo method for the pump having six rows at C=0.6, the approximate method overestimates as much as 36% in the maximum compression ratio and 19% in the maximum pumping speed than does the Mote-Carlo method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.99-109
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• 2009

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.

• Korean Journal of Optics and Photonics
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• v.22 no.4
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• pp.161-169
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• 2011

In this paper, a tolerance allocation method using Monte-Carlo simulation with measured reflective eccentricity for high-sensitive IR optics is proposed. During optics fabrication and alignment, reflective eccentricity was measured using an optical centration measurement instrument. A Monte-Carlo simulation was performed using measured eccentricity data, and it gives statistical estimated performance of the optics after fabrication. The validity of the proposed tolerance allocation method was verified comparing the estimated MTF result with the measured MTF result of the fabricated optics.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.135-142
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• 2004

The severe frontal crash tests are NCAP with belted occupant at 35mph and FMVSS 208 with unbelted occupant at 25mph, This paper describes the design process of occupant protection systems, airbag and seat belt, under the two tests. In this study, NCAP simulations are performed by Monte Carlo search method and cluster analysis. The Monte Carlo search method is a global optimization technique and requires execution of a series of deterministic analyses, The procedure is as follows. 1) Define the region of interest 2) Perform Monte Carlo simulation with uniform distribution 3) Transform output to obtain points grouped around the local minima 4) Perform cluster analysis to obtain groups that are close to each other 5) Define the several feasible design ranges. The several feasible designs are acquired and checked under FMVSS 208 simulation with unbelted occupant at 25mph.