• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.169-171
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• 2001

This paper presents a method fer evaluation nodal probabilistic congestion and reliability indices of transmission systems using Monte Carlo simulation methods. Quantitative evaluation of transmission system reliability is very important because successful operation of an electric power system. In the deregulated electricity market depends on transmission system reliability management Monte Carlo methods are often preferable, when complex operating conditions are involved and/or the number of sever events is relatively large. To evaluate the reliability of a real power system, Monte Carlo Methods will be more useful. The characteristics and effectiveness of this methodology are illustrated by the case study using a small test system.

• Journal of the Korean Chemical Society
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• v.47 no.1
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• pp.31-37
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• 2003

The expanded uncertainties calculated by the application of GUM -approximation and Monte-Carlo simulation were compared about the model equation of one-point calibration which is widely used for the measurements and chemical analysis. For the comparisons, we assumed a set of artificial data at the various level of concentration and dispersion of t or normal distribution. Mistakes of more then 50 % was revealed at the values calculated by GUM-approximation in comparison with those of Monte-Carlo simulation because of the excess dispersion from t-distribution and non-linearity by division in the equation. In contrary, the mistake of calculation due to non-linearity of the equation was not observed in the level of detection limits with the equation of one-point calibration, because of the relatively large values of uncertainty in response.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.780-786
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• 2013

In the reliability-based design optimization of electromagnetic devices, the accurate and efficient reliability assessment method is very essential. The first-order sensitivity-assisted Monte Carlo Simulation is proposed in the former research. In order to improve its accuracy for wide application, in this paper, the second-order sensitivity analysis is presented by using the hybrid direct differentiation-adjoint variable method incorporated with the finite element method. By combining the second-order sensitivity with the Monte Carlo Simulation method, the second-order sensitivity-assisted Monte Carlo Simulation algorithm is proposed to implement reliability calculation. Through application to one superconductor magnetic energy storage system, its accuracy is validated by comparing calculation results with other methods.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.E
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• pp.59-66
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• 1990

Abstract The formulation of the probabilistic finite element method was briefly reviewed. The method was implemented into a computer program for frame analysis which has the same analogy as finite element analysis. Another program for Monte Carlo simulation of finite element analysis was written. Two sample structures were assumed and analized. The characteristics of the second moment statistics obtained by the probabilistic finite element method was examined through numerical studies. The applicability and limitation of the method were also evaluated in comparison with the data generated by Monte Carlo simulation.

• Nuclear Engineering and Technology
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• v.41 no.4
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• pp.381-390
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• 2009

This article provides selects of outstanding problems in computational neutron transport, with some suggested approaches thereto, as follows: i) ray effect in discrete ordinates method, ii) diffusion synthetic acceleration in strongly heterogeneous problems, iii) method of characteristics extension to three-dimensional geometry, iv) fission source and $k_{eff}$ convergence in Monte Carlo, v) depletion in Monte Carlo, vi) nuclear data evaluation, and vii) uncertainty estimation, including covariance data.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.319-324
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• 2006

Release rate is one of the important items for the environmental impact assessment caused by radioactive materials in case of an accidental release from the nuclear facilities. In this study, the uncertainty of the estimated release rate is evaluated using Monte Carlo method. Gaussian plume model and linear programming are used for estimating the release rate of a source material. Tracer experiment is performed at the Yeoung-Kwang nuclear site to understand the dispersion characteristics. The optimized release rate was 1.56 times rather than the released source as a result of the linear programming to minimize the sum of square errors between the observed concentrations of the experiment and the calculated ones using Gaussian plume model. In the mean time, 95% confidence interval of the estimated release rate was from 1.41 to 2.53 times compared with the released rate as a result of the Monte Carlo simulation considering input variations of the Gaussian plume model. We confirm that this kind of the uncertainty evaluation for the source rate can support decision making appropriately in case of the radiological emergencies.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.1
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• pp.9-16
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• 2003

In this paper, we propose a reliability evaluation method considering the momentary interruptions of power distribution systems. The results of research are concentrated on two parts. One is the analytic and probabilistic reliability evaluation of power distribution system considering the momentary interruptions and the other is the reliability cost evaluation that unifies the cost of sustained and momentary interruptions. This proposed reliability cost evaluation methodology is also divided into the analytic and probabilistic approach and the time sequential Monte Carlo method is used for the probabilistic method. The proposed methods are tested using the modified RBTS (Roy Billinton Test System) form and historical reliability data of KEPCO (Korea Electric Power Corporation) system. Through the case studies, it is verified that the proposed reliability evaluation and its cost/worth assessment methodologies can be applied to the actual reliability studies.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.3
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• pp.127-133
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• 2009

In this study, dimensionless-cumulative rainfall curves were generated by multivariate Monte Carlo method. For generation of rainfall curve rainfall storms were divided and made into dimensionless type since it was required to remove the spatial and temporal variances as well as differences in rainfall data. The dimensionless rainfall curves were divided into 4 types, and log-ratio method was introduced to overcome the limitations that elements of dimensionless-cumulative rainfall curve should always be more than zero and the sum total should be one. Orthogonal transformation by Johnson system and the constrained non-normal multivariate Monte Carlo simulation were introduced to analyse the rainfall characteristics. The generative technique in stochastic rainfall variation using multivariate Monte Carlo method will contribute to the design and evaluation of hydrosystems and can use the establishment of the flood disaster prevention system.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.04a
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• pp.293-294
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• 1999

• Journal of Radiation Industry
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• v.6 no.1
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• pp.95-100
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• 2012

In this study, there has been investigated the simulation of irradiation dose using Monte Carlo methodology for the biological control of wooden cultural property. In the evaluation of fungal contamination on wooden cultural properties, Cladosporium tenuissimum, Aspergillus versicolor, Penicillium sp. were mainly identified from the Gimjeotgae. But these microorganisms were completely inactivated by 20 kGy gamma-rays. For dosimetry simulation of wooden cultural properties, Monte Carlo methodology with MCNP was used. The radiation absorbed dose distribution was predicted at 8.2~18.9 kGy. These results show that irradiation is effective for biologic control of wooden cultural properties and Monte Carlo methodology is useful for non-destructive conservation and preservation of wooden cultural properties.