• Nuclear Engineering and Technology
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• v.44 no.2
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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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• v.54 no.3
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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.

• 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.

• Structural Engineering and Mechanics
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• v.32 no.1
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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.

• Journal of Industrial Technology
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• v.22 no.B
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.2
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• pp.58-64
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• 2008

In this paper, we proposed the methodology of interference analysis based on monte-carlo method for effective use of Industrial, Scientific, Medical (ISM) band. The interference scenario is divided according to the distance and density. The simulation of interference analysis evaluates the interference probability according to distribution density of Interfering Transmitters (It) in the Secure Interference Area (SIA). The SIA is gained from the Interference Efficiency Range that satisfied to Interference Permissible Range of Victim Receiver (Vr). Simulation result that apply the proposed interference scenario to the WLAN and bluetooth, Interference Permissible Range was 60~400m. And the WLAN was acceptable within interference permissible range to six bluetooth that exist in the SIA. In the same condition, when applied Listen Before Talk (LBT) based on Cognitive Radio (CR) to the bluetooth using Frequency Hopping (FH), interference probability was decreased sharply. The Spectrum Engineering Advanced Monte Carlo Analysis Tool (SEAMCAT) that has been developed based on the monte-carlo method by European Radio-communications Office (ERO) were used to the interference simulation.

• Economic and Environmental Geology
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• v.50 no.3
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• pp.239-250
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• 2017

Physically based landslide susceptibility analysis has been recognized as an effective analysis method because it can consider the mechanism of landslide occurrence. The physically based analysis used the slope geometry and geotechnical properties of slope materials as input. However, when the physically based approach is adopted in regional scale area, the uncertainties were involved in the analysis procedure due to spatial variation and complex geological conditions, which causes inaccurate analysis results. Therefore, probabilistic method have been used to quantify these uncertainties. However, the uncertainties caused by lack of information are not dealt with the probabilistic analysis. Therefore, fuzzy set theory was adopted in this study because the fuzzy set theory is more effective to deal with uncertainties caused by lack of information. In addition, the vertex method and Monte Carlo simulation are coupled with the fuzzy approach. The proposed approach was used to evaluate the landslide susceptibility for a regional study area. In order to compare the analysis results of the proposed approach, Monte Carlo simulation as the probabilistic analysis and the deterministic analysis are used to analyze the landslide susceptibility for same study area. We found that Fuzzy Monte Carlo simulation showed the better prediction accuracy than the probabilistic analysis and the deterministic analysis.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.43-51
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• 2004

In order to decommission the shielding concrete of KRR(Korea Research Reactor) -1＆2, it must be exactly determined activated level and range by neutron irradiation during operation. To determine the activated level and range, it must be sampled and analyzed the core sample. But, there are difficulties in sample preparation and determination of the measurement efficiency by self-absorption. In the study, the full energy efficiency of the HPGe detector was compared with the measured value using standard source and the calculated one using Monte Carlo simulation. Also. self-absorption effects due to the density and component change of the concrete were calculated using the Monte Carlo method. Its results will be used radioactivity analysis of the real concrete core sample in the future.

• Structural Engineering and Mechanics
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• v.34 no.4
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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.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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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.