• 전기학회논문지P
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• 제56권1호
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• pp.1-5
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• 2007

Study on the electron transport coefficient in mixtures of CF4 and Ar, have been analyzed over a range of the reduced electric field strength between 0.1 and 350[Td] by the two-term approximation of the Boltzmann equation (BEq.) method and the Monte Carlo simulation (MCS). The calculations of electron swarm parameters require the knowledge of several collision cross-sections of electron beam. Thus, published momentum transfer, ionization, vibration, attachment, electronic excitation, and dissociation cross-sections of electrons for $CF_4$ and Ar, were used. The differences of the transport coefficients of electrons in $CF_4$ mixtures of Ar, have been explained by the deduced energy distribution functions for electrons and the complete collision cross-sections for electrons. The results of the Boltzmann equation and the Monte Carlo simulation have been compared with the data presented by several workers. The deduced transport coefficients for electrons agree reasonably well with the experimental and simulation data obtained by Nakamura and Hayashi. The energy distribution function of electrons in $CF_4-Ar$ mixtures shows the Maxwellian distribution for energy. That is, $f({\varepsilon})$ has the symmetrical shape whose axis of symmetry is a most probably energy. The proposed theoretical simulation techniques in this work will be useful to predict the fundamental process of charged particles and the breakdown properties of gas mixtures. A two-term approximation of the Boltzmann equation analysis and Monte Carlo simulation have been used to study electron transport coefficients.

• 대한전기학회논문지:전력기술부문A
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• 제53권2호
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• pp.121-128
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• 2004

This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using sequential Monte Carlo Simulation (MCS) techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential MCS to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. In addition, the mathematical formulation for DLC programs' economic evaluations are developed. To show the efficiency and effectiveness of the suggested method, the numerical studies have been performed for the modified IEEE reliability test system.

• Steel and Composite Structures
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• 제8권2호
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• pp.129-144
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• 2008

This paper demonstrates an application of the perturbation based stochastic finite element method (SFEM) for predicting the performance of structural systems made of composite sections with random material properties. The composite member consists of materials in contact each of which can surround a finite number of inclusions. The perturbation based stochastic finite element analysis can provide probabilistic behavior of a structure, only the first two moments of random variables need to be known, and should therefore be suitable as an alternative to Monte Carlo simulation (MCS) for realizing structural analysis. A summary of stiffness matrix formulation of composite systems and perturbation based stochastic finite element dynamic analysis formulation of structural systems made of composite sections is given. Two numerical examples are presented to illustrate the method. During stochastic analysis, displacements and sectional forces of composite systems are obtained from perturbation and Monte Carlo methods by changing elastic modulus as random variable. The results imply that perturbation based SFEM method gives close results to MCS method and it can be used instead of MCS method, especially, if computational cost is taken into consideration.

• Structural Engineering and Mechanics
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• 제66권6호
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• pp.677-691
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• 2018

Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.

• Journal of Electrical Engineering and Technology
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• 제8권2호
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• pp.331-338
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• 2013

A new reliability calculation method is proposed based on design sensitivity analysis by the finite element method for nonlinear performance constraints in the optimal design of electromagnetic devices. In the proposed method, the reliability of a given design is calculated by using the Monte Carlo simulation (MCS) method after approximating a constraint function to a linear one in the confidence interval with the help of its sensitivity information. The validity and numerical efficiency of the proposed sensitivity-assisted MCS method are investigated by comparing its numerical results with those obtained by using the conventional MCS method and the first-order reliability method for analytic functions and the TEAM Workshop Problem 22.

• 대한환경공학회지
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• 제30권11호
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• pp.1123-1131
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• 2008

본 연구는 대기 중 PM2.5의 미량금속 중 As을 중성자방사화분석법을 이용하여 분석할 때 발생되는 측정불확도를 ISO GUM 방법과 MCS 방법을 모두 적용하여 비교, 평가하였다. 불확도의 요인은 ISO GUM을 엄격하게 준용하여 파악하였으며 특정일에 채취된 PM2.5 내 As 농도에 대해 두 방법의 계산 결과가 4% 미만으로 크게 다르지 않는 것으로 나타났다. 연구기간 중 채취된 총 60개의 PM2.5 시료에 대해 As 농도의 확장불확도를 역시 MCS 방법을 이용하여 산출하였는데, 연구지역에서의 As의 개별 농도값에 대한 95% 신뢰구간의 확장불확도는 대부분 10%의 범위에서 존재하는 것으로 나타났다. 확장불확도에 대한 표준불확도 요인의 기여율은 계측통계오차(62.3%), 검출효율(18.5%), 시료 채취 시 유량(12.3%), flux 변동(2.3%), 특정감마선 방출률(1.8%) 등의 순으로 크게 나타났다.

• 한국지반공학회논문집
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• 제32권11호
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• pp.31-42
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• 2016

지진에 대한 사면안정 해석은 지진에 의한 관성력을 정적하중으로 고려하는 유사정적해석을 널리 사용하고 있다. 사면과 같은 지반 구조물은 지반정수의 불확실성이 포함되어 있어 확률론적 해석을 이용하여 지반정수의 불확실성을 고려해야 한다. 본 연구에서는 지반의 불확실성을 고려한 확률론적 사면안정해석을 수행하였으며, 구조물이 임의 수준의 지반 운동을 받을 때 파괴상태에 도달하는 확률을 그래프로 나타낸 취약도 곡선을 작성하였다. 유사정적해석으로 확률론적 사면안정해석을 수행하기 위해 Monte Carlo Simulation(MCS)을 시행하였다. MCS의 소요 시간을 단축하기 위하여 인공신경망 기반의 응답면 기법을 이용해 파괴확률을 산출하여 수평지진계수별 취약도 곡선을 작성하는 방법을 제시하였다. 인공신경망을 이용하여 작성한 취약도 곡선을 MCS의 결과와 비교해 본 결과 상당한 시간 절약에 비해 유사한 결과를 얻을 수 있었다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1999년도 춘계학술대회 논문집
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• pp.696-699
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• 1999

The electron transport coefficients in $SF_6+N_2$ gas is analysed in range of E/N values from 100~900(Td) by a Monte Carlo simulation and Boltzmann method, using a set of electron collision cross sections determined by the authors. The result of the Monte Carlo simulation such as electron drift velocity, ionization and electron attachment coefficients, longitudinal and transverse diffusion coefficients in nearly agreement with the respective experimental and theoretical for a range of E/N.

• Structural Engineering and Mechanics
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• 제21권5호
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• pp.507-518
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• 2005

In the shear-lag analysis of structures deterministic procedure is insufficient to provide complete information. Probabilistic analysis is a holistic approach for analyzing shear-lag effects considering uncertainties in structural parameters. This paper proposes an efficient and accurate algorithm to analyze shear-lag effects of structures with parameter uncertainties. The proposed algorithm integrated the advantages of the response surface method (RSM), finite element method (FEM) and Monte Carlo simulation (MCS). Uncertainties in the structural parameters can be taken into account in this algorithm. The algorithm is verified using independently generated finite element data. The proposed algorithm is then used to analyze the shear-lag effects of a simply supported beam with parameter uncertainties. The results show that the proposed algorithm based on the central composite design is the most promising one in view of its accuracy and efficiency. Finally, a parametric study was conducted to investigate the effect of each of the random variables on the statistical moment of structural stress response.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2009년도 춘계 학술대회 제21권1호
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• pp.301-302
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• 2009

콘크리트 구조물의 성능 저하 현상 증의 하나인 탄산화에 의한 내구성능 저하를 평가하는 방법으로 각 설계변수의 불확실성윤 고려하기 위하여 MCS(Monte Carlo Simulation) 기법을 적용하여 탄산화의 영향을 받는 철근콘크리트 구조물의 내구성 해석을 수행하고 그 결과를 분석하였다. 일반적인 경우 50년 경과시 부식확률 10%에 이르는 필요피복두께는 탄산화 속도계수가 4mm/$year^{0.5}$일 경우 최소 53mm가 요구되는 것으로 나타났다. 추후 설계변수의 통계적인 성질에 대한 추가적인 연구를 통하여 탄산화 과정의 불확실성윤 합리적으로 고려함 수 있는 내구성 해석방법을 정립할 수 있을 것이다.