• Communications of the Korean Mathematical Society
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• v.22 no.3
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• pp.453-464
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• 2007

A computationally efficient numerical scheme is presented for the phase-field model of two-phase systems for anisotropic interfacial energy. The scheme is solved by using a nonlinear multigrid method. When the coefficient for the anisotropic interfacial energy is sufficiently high, the interface of the system shows corners or missing crystallographic orientations. Numerical simulations with high and low anisotropic coefficients show excellent agreement with exact equilibrium shapes. We also present spinodal decomposition, which shows the robustness of the pro-posed scheme.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05b
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• pp.505-510
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• 1996

A simple model for RAM (Reliability, Availability and Maintainability) analysis and its computer code are developed for application to DUPIC fuel fabrication system. The approach is obtained by linking the allocation model (top-down method) to bottom-up method for RAM analysis. As a result, the availability requirement of subsystem, as well as the buffer storage requirement between processes, are evaluated for the DUPIC facility..

• Korean System Dynamics Review
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• v.6 no.2
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• pp.95-116
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• 2005

The purpose of this study is to analyze the structure of renewable energy market in order to deploy more renewable energy in Korea on the basis of information asymmetry between suppliers and demanders. To attain this purpose we develop the model to analyze and simulate the renewable market using system dynamics. This model is developed not to forecast the accurate size of market but to learn more structure of market using our limited data, mental model and knowledge of market.

• Korean Chemical Engineering Research
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• v.53 no.6
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• pp.746-754
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• 2015

In this study, we investigated the effects of the temperature on the coal/biomass $char-CO_2$ gasification reaction under isothermal conditions of $700{\sim}900^{\circ}C$ using the lignite(Indonesia Eco coal) with biomass (korea cypress). Ni catalysts were impregnated on the coal by the ion-exchange method. Four kinetic models which are shrinking core model (SCM), volumetric reaction model (VRM), random pore model (RPM) and modified volumetric reaction model (MVRM) for gas-solid reaction were applied to the experimental data against the measured kinetic data. The Activation energy of Ni-coal/biomass, non-catalyst coal/biomass $Char-CO_2$ gasification was calculated from the Arrhenius equation.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Elastomers and Composites
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• v.51 no.4
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• pp.308-316
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• 2016

The rubber materials are widely used in automobile industry due to their capability of a large amount of elastic deformation under a force. Current trend of design process requires prediction of functional properties of parts at early stage. The behavior of rubber material can be modeled using strain energy density function. In this study, five different strain energy density functions - Neo-Hookean model, Reduced Polynomial $2^{nd}$ model, Ogden $3^{rd}$ model, Arruda Boyce model and Van der Waals model - were used to estimate the behavior of carbon black added natural rubber under uniaxial load. Two kinds of tests - uniaxial tension test and biaxial tension test - were performed and used to correlate the coefficients of the strain energy density function. Numerical simulations were carried out using finite element analysis and compared with experimental results. Simulation revealed that Ogden $3^{rd}$ model predicted the behavior of carbon added natural rubber under uniaxial load regardless of experimental data selection for coefficient correlation. However, Reduced Polynomial $2^{nd}$, Ogden $3^{rd}$, and Van der Waals with uniaxial tension test and biaxial tension test data selected for coefficient correlation showed close estimation of behavior of biaxial tension test. Reduced Polynomial $2^{nd}$ model predicted the behavior of biaxial tension test most closely.

• Journal of Environmental Science International
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• v.19 no.10
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• pp.1229-1235
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• 2010

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

• Journal of the Korean Solar Energy Society
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• v.25 no.1
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• pp.97-104
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• 2005

A one-dimensional heat transfer model for the vertical borehole system is derived in this study to predict the thermal behavior of the system and surrounding soil. In this model the U-tube is replaced with one effective tube of effective diameter which is surrounded by concentric grout region. All thermal resistances of borehole are counted in the grout region with effective thermal conductivity of grout. Effective thermal conductivity of grout and sand are calculated through parameter estimation. The validity of this model is accomplished through comparison of the predicted temperature profiles of the model with experimental data.

• Journal of Radiation Protection and Research
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• v.28 no.3
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• pp.239-245
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• 2003

Every nuclear power plant measured concentrations of tritium in groundwater and surface water around the plants periodically. It was not easy to predict the tritium concentration only with these measurement data in case of various release scenarios. KAERI developed a new approach to find the relationship between the tritium release rate and tritium concentration in the environment. The approach was based upon a dynamic compartment model. In this paper the dynamic compartment model was modified to predict the tritium behavior more accurately. The mechanisms considered for the transfer of tritium between the compartments were evaporation, groundwater flow, infiltration, runoff, and hydrodynamic dispersion. Time dependent source terms of the compartment model were introduced to refine the release scenarios. Also, transfer coefficients between the compartments were obtained using realistic geographical data. In order to illustrate the model various release scenarios were developed, and the change of tritium concentration in groundwater and surface water around the nuclear power plants was estimated.