• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.161-167
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• 2021

This paper presents the method to quantitatively evaluate the uncertainty of the semi-empirical Bidirectional Reflectance Distribution Function (BRDF) model for Himawari-8/AHI. The uncertainty of BRDF modeling was affected by various issues such as assumption of model and number of observations, thus, it is difficult that evaluating the performance of BRDF modeling using simple uncertainty equations. Therefore, in this paper, Monte-Carlo method, which is most dependable method to analyze dynamic complex systems through iterative simulation, was used. The 1,000 input datasets for analyzing the uncertainty of BRDF modeling were generated using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) Radiative Transfer Model (RTM) simulation with MODerate Resolution Imaging Spectroradiometer (MODIS) BRDF product. Then, we randomly selected data according to the number of observations from 4 to 35 in the input dataset and performed BRDF modeling using them. Finally, the uncertainty was calculated by comparing reproduced surface reflectance through the BRDF model and simulated surface reflectance using 6S RTM and expressed as bias and root-mean-square-error (RMSE). The bias was negative for all observations and channels, but was very small within 0.01. RMSE showed a tendency to decrease as the number of observations increased, and showed a stable value within 0.05 in all channels. In addition, our results show that when the viewing zenith angle is 40° or more, the RMSE tends to increase slightly. This information can be utilized in the uncertainty analysis of subsequently retrieved geophysical variables.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.353-362
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• 2014

The effects of nuclear data uncertainties are studied on a typical PWR fuel assembly model in the framework of the OECD Nuclear Energy Agency UAM (Uncertainty Analysis in Modeling) expert working group. The "Fast Total Monte Carlo" method is applied on a model for the Monte Carlo transport and burnup code SERPENT. Uncertainties on $k_{\infty}$, reaction rates, two-group cross sections, inventory and local pin power density during burnup are obtained, due to transport cross sections for the actinides and fission products, fission yields and thermal scattering data.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3639-3647
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• 2023

This paper defines a 3D full core neutronics benchmark which is based on the NuScale small modular reactor (SMR) concept. The paper provides a detailed description of the NuScale-like core, a list of expected outputs, and a reference solution to the benchmark exercises obtained with the Monte Carlo code Serpent. The benchmark was developed in the framework of the Euratom McSAFER project and can be used for verification of computational chains dedicated to 3D full-core neutronics simulations of water cooled SMRs. The paper is supplemented with a digital data set to ease the modeling process.

• ETRI Journal
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• v.23 no.1
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• pp.23-32
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• 2001

Examination of the statistical variation of integrated passive components is crucial for designing and characterizing the performance of multichip module (MCM) substrates. In this paper, the statistical analysis of parallel plate capacitors with gridded plates manufactured in a multilayer low temperature cofired ceramic (LTCC) process is presented. A set of integrated capacitor structures is fabricated, and their scattering parameters are measured for a range of frequencies from 50 MHz to 5 GHz. Using optimized equivalent circuits obtained from HSPICE, mean and absolute deviation is calculated for each component of each device model. Monte Carlo Analysis for the capacitor structures is then performed using HSPICE. Using a comparison of the Monte Carlo results and measured data, it is determined that even a small number of sample structures, the statistical variation of the component values provides an accurate representation of the overall capacitor performance.

• Journal of computational fluids engineering
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• v.4 no.3
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• pp.63-70
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• 1999

A Direct simulation Monte-Carlo (DSMC) code is developed, which employs the Monte-Carlo statistical sampling technique to investigate hypersonic rarefied gas flows accompanying chemical reactions. The DSMC method is a numerical simulation technique for analyzing the Boltzmann equation by modeling a real gas flow using a representative set of molecules. Due to the limitations in computational requirements. the present method is applied to a flow around a simple two-dimensional object in exit velocity of 7.6 km/sec at an altitude of 90 km. For the calculation of chemical reactions an air model with five species (O₂, N₂, O, N, NO) and 19 chemical reactions is employed. The simulated result showed various rarefaction effects in the hypersonic flow with chemical reactions.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.731-734
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• 2003

An atomistic process modeling, Kinetic Monte Carlo simulation, has the advantage of being both conceptually simple and extremely powerful. Instead of diffusion equations, it is based on the definitions of the interactions between individual atoms and defects. Those interactions can be derived either directly from molecular dynamics, first principles calculations, or from experiment. In this paper, as a simple illustration of the kinetic Monte Carlo we simulate defects (self-interstitials and vacancies) diffusion after ion implantation in Si crystalline.

• Journal of the Optical Society of Korea
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• v.19 no.5
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• pp.444-448
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• 2015

We describe the radiative transfer of a Gaussian beam in a water medium using the Monte Carlo method offering basic propagation behaviors. The simulation shows how the energy of the initial Gaussian beam is redistributed as it propagates in coastal water, and also depicts the dependence of the propagation behavior on inherent optical properties of the ocean water such as the single scattering albedo as well as on laser beam parameters, e.g. the M squared. Our results may widen the applicability of LIDARs by providing a couple of design considerations for a bathymetric LIDAR.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1269-1275
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• 2013

Recently, many studies have suggested that an electric vehicle (EV) is one of the means for increasing the reliability of power systems in new energy environments. EVs can make a contribution to improving reliability by providing frequency regulation in power systems in which the Vehicle-to-Grid (V2G) technology has been implemented and, if economically viable, can be helpful in increasing power system reliability. This paper presents a stochastic method for optimal coordination of charging and frequency regulation decisions for an EV aggregator using the Least Square Monte-Carlo (LSMC) with modeling of electricity price uncertainty. The LSMC can be used to assess the value of options based on electricity price uncertainty in order to simultaneously optimize the scheduling of EV charging and regulation service for the EV aggregator. The results of a numerical example show that the proposed method can significantly improve the expected profits of an EV aggregator.