• Nuclear Engineering and Technology
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• 제44권2호
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• pp.151-160
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• 2012

The advantages for using Monte Carlo methods to analyze full-core reactor configurations include essentially exact representation of geometry and physical phenomena that are important for reactor analysis. But this substantial advantage comes at a substantial cost because of the computational burden, both in terms of memory demand and computational time. This paper focuses on the challenges facing full-core Monte Carlo for keff calculations and the prospects for Monte Carlo becoming a routine tool for reactor analysis.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.790-802
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• 2022

Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimate important reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitably result in some uncertainty in calculated quantities. The present study includes parametric uncertainty quantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC) facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlo uncertainty when performing these analyses. This approach for UQ/SA includes consideration of Monte Carlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measured parameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtained from a surrogate model. These methodologies are applied to the uncertainty and sensitivity of k_eff for two sets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involving a linear surrogate model provides accurate estimations for k_eff uncertainty and the Monte Carlo uncertainty in calculated k_eff values can have a large effect on computed linear model parameters for parameters with low influence on k_eff.

• 한국농공학회지
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• 제32권E호
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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.

• Structural Engineering and Mechanics
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• 제32권1호
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• pp.1-20
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• 2009

The present contribution addresses uncertainty quantification and uncertainty propagation in structural mechanics using stochastic analysis. Presently available procedures to describe uncertainties in load and resistance within a suitable mathematical framework are shortly addressed. Monte Carlo methods are proposed for studying the variability in the structural properties and for their propagation to the response. The general applicability and versatility of Monte Carlo Simulation is demonstrated in the context with computational models that have been developed for deterministic structural analysis. After discussing Direct Monte Carlo Simulation for the assessment of the response variability, some recently developed advanced Monte Carlo methods applied for reliability assessment are described, such as Importance Sampling for linear uncertain structures subjected to Gaussian loading, Line Sampling in linear dynamics and Subset simulation. The numerical example demonstrates the applicability of Line Sampling to general linear uncertain FE systems under Gaussian distributed excitation.

• 산업기술연구
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• 제22권B호
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• pp.3-12
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• 2002

Monte-Carlo simulation technique has advantages over deterministic simulation in various engineering analysis since Monte-Carlo simulation can take into consideration of scattering of various design variables, which is inherent characteristics of physical world. In this work, Monte-Carlo simulation of steady-state cornering behavior of a truck with design variables like hard points and busing stiffness. The purpose of the simulation is to improve understeer gradient of the truck, which exhibits a small amount of instability when the lateral acceleration is about 0.4g. Through correlation analysis, design variables that have high impacts on the cornering behavior were selected, and significant performance improvement has been achieved by appropriately changing the high impact design variables.

• 한국전자통신학회논문지
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• 제3권2호
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• pp.58-64
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• 2008

본 논문에서는 industrial, scientific, medical (ISM) 대역의 효율적인 운용을 위해 간섭시나리오와 monte-carlo method 기반의 간섭분석 methodology를 제안하였다. 간섭시나리오는 거리와 밀도에 따른 시나리오로 구분되며, 희생원을 victim receiver (Vr), 간섭원을 interfering transmitter (It)로 정의한다. 간섭분석 시뮬레이션은 Vr의 간섭허용치를 만족하는 It의 간섭유효구간을 통해 얻어지는 유효간섭영역 내에서 It의 분포밀도에 따른 간섭 확률을 도출하게 된다. 제안된 간섭시나리오를 적용하여 2.4GHz ISM 대역에서 사용되는 대표적 무선설비인 WLAN (Vr)과 bluetooth (It)의 시뮬레이션 결과 간섭유효구간은 60~400m 이며, WLAN은 유효간섭영역에 존재하는 6개의 bluetooth와 간섭허용치를 만족하는 범위 내에서 운용될 수 있음을 확인하였다. 또한, 같은 조건에서 frequency hopping (FH) 통신방식을 사용하는 bluetooth에 cognitive radio(CR) 기술 기반의 listen before talk (LBT) 방식을 적용시 간섭 확률은 크게 감소하였다. 컴퓨터 시뮬레이션은 european radiocommunications office (ERO)에서 monte-carlo method를 기반으로 개발한 spectrum engineering advanced monte carlo analysis tool (SEAMCAT)을 사용하였다.

• 자원환경지질
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• 제50권3호
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• pp.239-250
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• 2017

정량적인 산사태 취약성 분석 중 물리 모델 기반의 분석(physically based approach)은 산사태의 발생 메커니즘 과정을 고려할 수 있는 장점으로 인해 다양한 취약성 분석기법 중 가장 효과적인 기법으로 알려져 있다. 물리 모델 분석은 사면의 지형학적 및 지질공학적 특성과 관련된 입력 자료들을 활용하는데, 현장으로부터 지질공학적 특성을 획득하는 과정에서 지반의 공간적 변동성과 복잡한 지질조건으로 인해 불확실성이 발생하며 이는 부정확한 결과를 초래한다. 따라서 이러한 불확실성을 정량화하기 위하여 확률론적 기법이 활용되어 왔다. 그러나 확률론적 분석을 수행하기 위해 필요한 입력변수의 확률특성은 현장 조사나 실험에서의 수량 제약으로 인하여 정확하게 파악하기 힘들다는 문제가 발생한다. 따라서 본 연구에서는 이러한 원인으로 인해 발생하는 불확실성을 다루기 위하여 퍼지집합이론(fuzzy set theory)을 활용하였다. 특히, 본 연구에서는 퍼지집합이론과 몬테카를로기법(Monte Carlo simulation)을 결합한 분석기법을 제안하였고 이를 실제 산사태가 발생한 연구지역에 적용하여 적정성을 파악하였다. 이를 위하여 1998년 8월 대규모의 산사태가 발생한 경상북도 상주시 일대를 연구지역으로 선정하고 산사태 취약성 분석을 수행하였다. 또한 퍼지몬테카를로기법(Fuzzy Monte Carlo simulation)의 예측 정확도 비교를 위해, 기존의 확률론적 기법인 몬테카를로기법(Monte Carlo simulation)과 안전율 수행 결과와 비교분석 하였다. 그 결과 퍼지몬테카를로기법(Fuzzy Monte Carlo simulation)이 다른 기법에 비해 가장 좋은 예측의 정확도를 보였다.

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2004년도 학술논문집
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• pp.43-51
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• 2004

현재 해체가 진행 중인 연구로 1. 2호기의 원자로 차폐 콘크리트를 해체하기 위해서는 운전기간동안 중성자 조사에 의한 방사화 정도 및 범위를 정확하게 결정하여야 한다. 차폐 콘크리트의 방사화 정도 및 범위를 결정하기 위해서 코어 시료를 채취하여 분석하여야 하는데, 시료 전처리의 어려움과 표준선원의 준비 및 자체흡수효과에 의하여 정확한 측정효율을 결정하는데 어려움이 있다. 본 연구에서는 방사능 분석에 이용되는 HPGe 검출기의 전에너지 검출 효율을 표준선원을 이용한 측정값과 Monte Carlo 방법을 이용하여 계산 값을 비교하였다. 또한, Monte Carlo 모사 기법을 이용하여 콘크리트의 밀도 및 성훈 변화에 따른 자체흡수 효과를 계산하였으며, 향후 실제 콘크리트 코어 시료를 채취 시 방사능 분석에 이용할 계획이다.

• Structural Engineering and Mechanics
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• 제34권4호
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• pp.417-447
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• 2010

The uncertainty often observed in experimental strengths of masonry constituents makes critical the selection of the appropriate inputs in finite element analysis of complex masonry buildings, as well as requires modelling the building ultimate load as a random variable. On the other hand, the utilization of expensive Monte Carlo simulations to estimate collapse load probability distributions may become computationally impractical when a single analysis of a complex building requires hours of computer calculations. To reduce the computational cost of Monte Carlo simulations, direct computer calculations can be replaced with inexpensive Response Surface (RS) models. This work investigates the use of RS models in Monte Carlo analysis of complex masonry buildings with random input parameters. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models, show how the proposed approach could be a useful tool in problems of technical interest.

• 한국정밀공학회지
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• 제21권2호
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• pp.92-99
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• 2004

The errors can cause the serious loss of the performance of a precision machine system. In this paper, we proposed the method of allocating the alignment tolerances of the parts and applied this method to get the optimal tolerances of a Confocal Scanning Microscope. In general, tight tolerances are required to maintain the performance of a system, but a high cost of manufacturing and assembling is required to preserve the tight tolerances. The purpose of allocating the optimal tolerances is minimizing the cost while keeping the high performance of the system. In the optimal problem, we maximized the tolerances while maintaining the performance requirements. The Monte Carlo Method, a statistical simulation method, is used in tolerance analysis. Alignment tolerances of optical components of the confocal scanning microscope are optimized to minimize the cost and to maintain the observation performance of the microscope. We can also apply this method to the other precision machine system.