• 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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.7
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• pp.657-664
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• 2011

This study proposes an inverse method for estimating the boundary temperature in a gas-filled, onedimensional parallel domain enclosed by parallel plates. The distance between the plates is considered submicron to one mm. In the current method, it is assumed that the conditions of both heat flux and temperature are simultaneously applicable to one boundary, while no conditions are applicable to the other boundary The temperature on one of the boundaries should be inversely determined from the known temperature and heat flux on the other boundary. This study proposes a procedure for estimating the unknown boundary temperature through Monte Carlo simulation. Both the forward and inverse problems employ the Monte Carlo approach. The forward (direct) problem is solved by using the direct simulation Monte Carlo while the inverse solution is obtained by the simulated annealing.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.5
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• pp.554-562
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• 2012

Processing of Scanning electron microscope imaging has been analyzed in both secondary electron (SE) imaging and backscattered electron (BSE) image. Because of unique characteristics of both secondary electron and backscattered electron image, mechanism of imaging process and image quality are quite different each other. For the sake of characterize imaging process, Monte Carlo simulation code have been developed. It simulates electron penetration and depth profile in certain material. In addition, secondary electron and backscattered electron generation process as well as their spatial distribution and energy characteristics can be simulated. Geometries that has fundamental feature have been imaged using the developed Monte Carlo code. Two, SE and BSE images generation process will be discussed. BSE imaging process can be readily used to discriminate in both material and geometry by simply changing position and direction of BSE detector. The developed MC code could be useful to design BSE detector and their position. Furthermore, surface reconstruction technique is possibly developed at the further research efforts. Basics of Monte Carlo simulation method will be discussed as well as characteristics of SE and BSE images.

• Smart Structures and Systems
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• v.11 no.1
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• pp.53-67
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• 2013

In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.

• 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.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.508-516
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• 1999

This paper presents a method for constructing composite power system effective load duration curves(CMELDC) at load points by Monte Carlo method. The concept of effective load duration curves(ELDC) in power system planning is useful and important in both HLII. CMELDC can be obtained from convolution integral processing of the probability function of unsupplied power and the load duration curve at each load point. This concept is analogy to the ELEC in HLI. And, the reliability indices (LOLP, EDNS) for composite power system are evaluated using CMELDC. Differences in reliability levels between HLI and HLII come from considering with the uncertainty associated with the outages of the transmission system. It is expected that the CMELDC can be applied usefully to areas such as reliability evaluation, probabilistic production cost simulation and analytical outage cost assessment, etc. in HLII, DC load flow and Monte Carlo method are used for this study. The characteristics and effectiveness of thes methodology are illustrated by a case study of the IEEE RTS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.4
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• pp.743-747
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• 2007

There has been steady effect for the development of the electron-beam lithography technologies for the circuit patterning of the future semiconductor devices. In this study, we have performed a Monte-Carlo simulation whore $1{\times}10^4$ electrons with various kinetic energies (100eV, 300eV, 500eV, 700eV, and 1000eV) were shot into polymethyl methacrylate(PMMA) resist of 100-nm thickness. The penetration depth of each electron beam in the resist layer were analyzed using Gaussian analysis method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3889-3903
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• 2017

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

• Journal of Ocean Engineering and Technology
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• v.34 no.5
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• pp.316-324
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• 2020

This paper presents comparative studies of reliability analysis and meta-modeling using the sampling method of Monte Carlo simulation for the structure design of an automatic ocean salt collector (AOSC). The thickness sizing variables of structure members are considered as random variables. Probabilistic performance functions are selected from strength performances evaluated via the finite element analysis of an AOSC. The sampling methods used in the comparative studies are simple random sampling and Sobol sequences with varied numbers of sampling. Approximation methods such as the Kriging model is applied to the meta-model generation. Reliability performances such as the probability failure and distribution are compared based on the variation of the sampling method of Monte Carlo simulation. The meta-modeling accuracy is evaluated for the Kriging model generated from the Monte Carlo simulation and Sobol sequence results. It is discovered that the Sobol sequence method is applicable to not only to the reliability analysis for the structural design of marine equipment such as the AOSC, but also to Kriging meta-modeling owing to its high numerical efficiency.