• Nuclear Engineering and Technology
• /
• v.56 no.3
• /
• pp.916-921
• /
• 2024

The control rod worth sensitivity to the neutron detector position in the rod drop experiment is studied by the time-dependent Monte Carlo (TDMC) neutron transport calculations for AGN-201K educational reactor and the Kyoto University Critical Assembly. The TDMC simulations of the rod drop experiments are conducted by the Seoul National University Monte Carlo (MC) code, McCARD, yielding time-dependent neutron densities at detector positions. The detector-position-dependent results of the total control rod worth calculated by the extrapolation, the integral counting, and the inverse methods are compared with the numerical reference using the MC eigenvalue calculations and the experimental results. From these comparisons, it is observed that the total control rod worth can be estimated with a considerable difference depending on the detector position through the rod drop experiment. The proposed TDMC simulation of the rod drop experiment can be applied for searching a better detector position or quantifying a bias for the control rod worth measurement.

• Journal of the Korea Society for Simulation
• /
• v.10 no.3
• /
• pp.31-46
• /
• 2001

Repeated measurements on units under different conditions are common in biological and biomedical studies. In a number of growth and pharmacokinetic studies, the relationship between the response and the covariates is assumed to be nonlinear in some unknown parameters and the form remains the same for all units. Nonlinear random coefficient models are used to analyze such repeated measurement data. Extended least squares methods are proposed in the literature for estimating the parameters of the model. However, neither objective function has closed form expression in practice. This paper proposes Monte Carlo methods to estimate the objective functions and the corresponding estimators. A simulation study that compare various methods is included.

• Journal of the Korean Institute of Gas
• /
• v.19 no.5
• /
• pp.81-86
• /
• 2015

A deterministic stress-based methodology has traditionally been applied in pipeline design. Meanwhile, reliability based design and assessment (RBDA) methodology has been extensively applied in offshore or nuclear structures. Lately, the release of ISO standard on reliability based limit state methods for pipelines ISO16708 indicates that the RBDA methodology is one of the newest directions of natural gas pipeline design method. This paper presents a case study of the RBDA procedure for predicting the time-dependent failure probability of pipelines with corrosion defects, where separable Monte Carlo (SMC) method is applied in the reliability estimation for corroded pipeline instead of traditional, crude Monte Carlo(CMC) Method. The result shows the SMC method take advantage of improving accuracy in reliability calculation.

• The Korean Journal of Applied Statistics
• /
• v.24 no.2
• /
• pp.401-409
• /
• 2011

From a social network of n nodes connected by l lines, one may produce centrality measures such as closeness, betweenness and so on. In the past, the magnitude of n was around 1,000 or 10,000 at most. Nowadays, some networks have 10,000, 100,000 or even more than that. Thus, the scalability issue needs the attention of researchers. In this short paper, we explore random networks of the size around n = 100,000 by Monte-Carlo method and propose Monte-Carlo algorithms of computing closeness and betweenness centrality measures to study the small world properties of social networks.

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

• Proceedings of the Korean Society of Medical Physics Conference
• /
• 2002.09a
• /
• pp.291-293
• /
• 2002

Accurate dose calculation in radiation treatment planning is most important for successful treatment. Since human body is composed of various materials and not an ideal shape, it is not easy to calculate the accurate effective dose in the patients. Many methods have been proposed to solve the inhomogeneity and surface contour problems. Monte Carlo simulations are regarded as the most accurate method, but it is not appropriate for routine planning because it takes so much time. Pencil beam kernel based convolution/superposition methods were also proposed to correct those effects. Nowadays, many commercial treatment planning systems, including Pinnacle and Helax-TMS, have adopted this algorithm as a dose calculation engine. The purpose of this study is to verify the accuracy of the dose calculated from pencil beam kernel based treatment planning system Helax-TMS comparing to Monte Carlo simulations and measurements especially in inhomogeneous region. Home-made inhomogeneous phantom, Helax-TMS ver. 6.0 and Monte Carlo code BEAMnrc and DOSXYZnrc were used in this study. Dose calculation results from TPS and Monte Carlo simulation were verified by measurements. In homogeneous media, the accuracy was acceptable but in inhomogeneous media, the errors were more significant.

• Journal of Korea Port Economic Association
• /
• v.31 no.3
• /
• pp.1-14
• /
• 2015

This study utilizes Monte Carlo simulation to forecast the time charter rate of vessels, the three-month Libor interest rate, and the ship demolition price, to mitigate future uncertainties involving these factors. The simulation was performed 10,000 times to obtain an exact result. For the empirical analysis - based on considerations in ordering ships in 2010-a comparison between the Monte Carlo simulation-based stochastic discounted cash flow (DCF) method and traditional DCF methods was made. The analysis revealed that the net present value obtained through Monte Carlo simulation was lower than that obtained via regular DCF methods, alerting the owners to risks and preventing them from placing injudicious orders for ships. This research has implications in reducing the uncertainties that future shipping markets face, through the use of a stochastic DCF approach with relevant variables and probability methods.

• Communications for Statistical Applications and Methods
• /
• v.17 no.1
• /
• pp.99-106
• /
• 2010

In this paper, we present two methods for obtaining prediction intervals for the times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. A numerical example and a Monte Carlo simulation study are presented to illustrate the prediction methods.

• 한국디지털정책학회:학술대회논문집
• /
• 2006.06a
• /
• pp.87-109
• /
• 2006

The purpose of this paper is to examine the present performance evaluation methods and to make Monte Carlo Simulation Model for the IT-based Government innovation project. It is suggested the proper ways in applying of Monte Carlo Simulation Model by integration of present evaluation methods. It develops the theoretical framework for this paper, examining the existing literature on proposing an approach to the key concepts of the economic impact analysis methods. It examines the actual conditions of performance evaluation focusing on the It-based Government Innovation project. It considers how the simulation model is applied to the performance management in the public innovation project focusing on the framework, process and procedure of performance management.

• Journal of the Korean Statistical Society
• /
• v.31 no.1
• /
• pp.77-91
• /
• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.