• ETRI Journal
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• v.38 no.1
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• pp.141-148
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• 2016

This paper proposes a method to estimate directly the incoherent scattered intensity and radar cross section (RCS) from the effective permittivity of a random media. The proposed method is derived from the original concept of incoherent scattering. The incoherent scattered field is expressed as a simple formula. Therefore, to reduce computation time, the proposed method can estimate the incoherent scattered intensity and RCS of a random media. To verify the potential of the proposed method for the desired applications, we conducted a Monte-Carlo analysis using the method of moments; we characterized the accuracy of the proposed method using the normalized mean square error (NMSE). In addition, several medium parameters, such as the density of scatterers and analysis volume, were studied to understand their effect on the scattering characteristics of a random media. The results of the Monte-Carlo analysis show good agreement with those of the proposed method, and the NMSE values of the proposed method and Monte-Carlo analysis are relatively small at less than 0.05.

• The Journal of Engineering Geology
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• v.5 no.3
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• pp.301-308
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• 1995

In this study, a stochastic finite element method is proposed with a view to consider rock property variations in the analysis of structural behavior on underground caverns. Here, the Monte carlo simulation technique, which has been widely used in probabilistic applications in many engineering fields, is applied for the analysis of the effect rock property distribution. Using the newly developed computer program based on the above - mentioned method, the underground opening in biaxial stress field is analyzed considering the effect of material property variation.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.

• Journal of Astronomy and Space Sciences
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• v.15 no.2
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• pp.417-425
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• 1998

Monte Carlo analysis is performed for the first acquisition and tracking of the KOMP-SAT spacecrat in GSOC tracking station after separation from Taurus launch vehicle. The error bounds in position and velocity vector in Earth-fixed coordinate system at injection point are assumed based on the previous launch mission. Ten thousands injection orbital elements with normal distribution are generated and propagated for Monte Carlo analysis. The tracking antenna pointing errors at spacecraft rising time and closest approach time at German Space Operations Center(GSOC) Weiheim track-ing station are derived. Then the tracking antenna scanning angles are analyzed for acquisition and tracking of the KOMPSAT signal.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.301-302
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• 2009

Uncertainties in carbonation process of concrete structures are treated by probability-based durability analysis for carbonation using Monte Carlo simulation technique. The results requires the minimum cover thickness of 53mm for 10% of corrosion probability under 4mm/$year^{0.5}$ of carbonation coefficient. The more researches on statistical properties of design variables may give reliable durability analysis/design methods for carbonation of concrete structures.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.529-543
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• 2021

Inevitable source-uncertainties in geometry configuration, boundary condition, and material properties may deviate the structural dynamics from its expected responses. This paper aims to examine the influence of these uncertainties on the vibration of functionally graded beams. Finite element procedures are presented for Timoshenko beams and utilized to generate reliable datasets. A prerequisite to the uncertainty quantification of the beam vibration using Monte Carlo simulation is generating large datasets, that require executing the numerical procedure many times leading to high computational cost. Utilizing artificial neural networks to model beam vibration can be a good approach. Initially, the optimal network for each beam configuration can be determined based on numerical performance and probabilistic criteria. Instead of executing thousands of times of the finite element procedure in stochastic analysis, these optimal networks serve as good alternatives to which the convergence of the Monte Carlo simulation, and the sensitivity and probabilistic vibration characteristics of each beam exposed to randomness are investigated. The simple procedure presented here is efficient to quantify the uncertainty of different stochastic behaviors of composite structures.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4228-4237
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• 2023

The Improved Deterministic Truncation of Monte Carlo (iDTMC) is a powerful acceleration and variance reduction scheme in the Monte Carlo analysis. The concept of the iDTMC method and correlated sampling-based real variance estimation are briefly introduced. Moreover, the application of the iterative scheme to the correlated sampling is discussed. The iDTMC method is utilized in a 3-dimensional small modular reactor (SMR) model problem. The real variances of burnup-dependent criticality and power distribution are evaluated and compared with the ones obtained from 30 independent iDTMC calculations. The impact of the inactive cycles on the correlated sampling is also evaluated to investigate the consistency of the correlated sample scheme. In addition, numerical performances and sensitivity analysis on the real variance estimation are performed in view of the figure of merit of the iDTMC method. The numerical results show that the correlated sampling accurately estimates the real variances with high computational efficiencies.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.343-350
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• 2014

A probabilistic construction stage analysis using the Monte Carlo Simulation was performed to address the effects of uncertainty regarding the material properties, environmental factors, and applied forces. In the previous research, creep and shrinkage were assumed to be completely independent random variables. However, because of the common influencing factors in the material models for the creep and shrinkage estimation, strong correlation between creep and shrinkage can be presumed. In this paper, an Monte Carlo Simulation using CEB-FIB creep and shrinkage equations were performed to actually evaluate the correlation coefficient between two phenomena, and then another Monte Carlo Simulation to evaluate the statistical properties of axial strain affected by partially correlated random variables including the material properties, environmental factors, and applied forces. The results of Monte Carlo Simulation were compared with measured strains of a column on a first story in a 58-story building. Comparison indicated that the variation due to the uncertainty related with the material properties were most severe. And measured strains was within the range of mean+standard deviation.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.5
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• pp.93-102
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• 2009

A prediction of streamflow based on multi-objective function is presented to check the performance of Probability Distributed Model(PDM) in Miho stream basin, Chungcheongbuk-do, Korea. PDM is a lumped conceptual rainfall runoff model which has been widely used for flood prevention activities in UK Environmental Agency. The Monte Carlo Analysis Toolkit(MCAT) is a numerical analysis tools based on population sampling, which allows evaluation of performance, identifiability, regional sensitivity and etc. PDM is calibrated for five model parameters by using MCAT. The results show that the performance of model parameters(cmax and k(q)) indicates high identifiability and the others obtain equifinality. In addition, the multi-objective function is applied to PDM for seeking suitable model parameters. The solution of the multi-objective function consists of the Pareto solution accounting to various trade-offs between the different objective functions considering properties of hydrograph. The result indicated the performance of model and simulated hydrograph are acceptable in terms on Nash Sutcliffe Effciency*(=0.035), FSB(=0.161), and FDBH(=0.809) to calibration periods, validation periods as well.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3450-3463
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• 2023

In the two-step analysis of Pebble-Bed type High-Temperature Gas-Cooled Reactor (PB-HTGR), the lattice physics calculation for the generation of homogenized cross-sections is based on the fuel pebble. However, the randomly-dispersed fuel particles in the fuel pebble introduce double heterogeneity and randomness. Compared to the deterministic method, the Monte Carlo method which is flexible in geometry modeling provides a high-fidelity treatment. Therefore, the Monte Carlo code NECP-MCX is extended in this study to perform the lattice physics calculation of the PB-HTGR. Firstly, the capability for the simulation of randomly-dispersed media, using the explicit modeling approach, is developed in NECP-MCX. Secondly, the capability for the generation of the homogenized cross-section is also developed in NECP-MCX. Finally, simplified PB-HTGR problems are calculated by a two-step neutronics analysis tool based on Monte Carlo homogenization. For the pebble beds mixed by fuel pebble and graphite pebble, the bias is less than 100 pcm when compared to the high-fidelity model, and the bias is increased to 269 pcm for pebble bed mixed by depleted fuel pebble. Numerical results show that the Monte Carlo lattice physics calculation for the two-step analysis of PB-HTGR is feasible.