• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.1140-1151
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• 2023

Fully Ceramic Microencapsulated (FCM) fuel is emerging advanced fuel material for the future nuclear reactors. The fuel pellet in the FCM fuel is composed of matrix and a large number of TRistructural-ISOtopic (TRISO) fuel particles which are randomly dispersed in the SiC matrix. The minimum layer thickness in a TRISO fuel particle is on the order of 10^-5 m, and the length of the FCM pellet is on the order of 10^-2 m. Hence, the heat transfer in the FCM pellet is a multi-scale phenomenon. In this study, three multi-scale heat conduction models including the Multi-region Layered (ML) model, Multi-region Non-layered (MN) model and Homogeneous model for FCM pellet were constructed. In the ML model, the random distributed TRISO fuel particles and coating layers are completely built. While the TRISO fuel particles with coating layers are homogenized in the MN model and the whole fuel pellet is taken as the homogenous material in the Homogeneous model. Taking the results by the ML model as the benchmark, the abilities of the MN model and Homogenous model to predict the maximum and average temperature were discussed. It was found that the MN model and the Homogenous model greatly underestimate the temperature of TRISO fuel particles. The reason is mainly that the conventional equivalent thermal conductivity (ETC) models do not take the internal heat source into account and are not suitable for the TRISO fuel particle. Then the improved ETCs considering internal heat source were derived. With the improved ETCs, the MN model is able to capture the peak temperature as well as the average temperature at a wide range of the linear powers (165 W/cm~ 415 W/cm) and the packing fractions (20%-50%). With the improved ETCs, the Homogenous model is better to predict the average temperature at different linear powers and packing fractions, and able to predict the peak temperature at high packing fractions (45%-50%).

• 한국연소학회:학술대회논문집
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• 2014.11a
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• pp.59-62
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• 2014

Predicting coal combustion by computational fluid dynamics (CFD) requires a combination of complicated flow and reaction models for turbulence, radiation, particle flows, heterogeneous combustion, and gaseous reactions. There are various levels of models available for each of the phenomena, but the use of advanced models are significantly restricted in a large-scale boiler due to the computational costs and the balance of accuracy between adopted models. In this study, the influence of coal devolatilization model and turbulent mixing rate was assessed in CFD for a commercial boiler at 500 MWe capacity. For coal devolatilization, two models were compared: i) a simple model assuming single volatile compound based on proximate analysis and ii) advanced model of FLASHCHAIN with multiple volatile species. It was found out that the influence of the model was observed near the flames but the overall gas temperature and heat transfer rate to the boiler were very similar. The devolatilization rate was found not significant since the difference in near-flame temperature became noticeable when it was multiplied by 10 or 0.1. In contrast, the influence of turbulent mixing rate (constant A in the Magnussen model) was found very large. Considering the heat transfer rate and flame temperature, a value of 1.0 was recommended for the rate constant.

• Computers and Concrete
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• v.21 no.4
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• pp.385-397
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• 2018

This study is focused on the investigation for similitude the requirements between prototype and scaled models to determine the structural behavior of concrete load bearing systems. The scaling concept has been utilized in many engineering branches, has been assisted to engineers and scientists for obtain the behavior of the prototype by using scaled model. The scaling can be done for two purposes, either scaling up or scaling down depending upon the application. Because, scaled down models are the experimentation on scaled models is cheaper than huge structures. These models also provide facilities for experimental work. Similarity relationships between systems are created either by field equations of the system or by dimensional analysis. Within this study, similarity relationships were obtained by both methods. The similarity relations obtained are applied to different load bearing systems and it is determined that the similarity relation is a general expression. In this study, as an example, column, frame, cantilever beam and simple beam are chosen and 1/2, 1/5 and 1/10 scales are applied. The results are compared with the analytical results which are obtained by creating of the finite element models with SAP2000 software of different scaled load bearing systems. The analysis results of all systems are examined and it is determined that the scale factors are constant depending on the scale types for different load bearing systems.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.282-285
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• 2001

In general, the PSS/E program based on RMS mathematical models is used for analyzing the steady state and transient stability phenomena of full-scale large power system. Whereas, the EMTDC program unlike PSS/E, studies the specific reduced small-scale power systems as a basis of instantaneous value mathematical models and used to analyze the Electro-Magnetic transient characteristics. The PSS/E provides various control models such as exciter, governor, PSS models and TGOV5 model but there are few control models in EMTDC. In this paper, we developed EMTDC model for TGOV5 of a steam turbine and boiler which represents governor action, main, reheat and low-pressure effects, including boiler effects. The EMTDC model is developed by examining PSS/E control block and using User Define Model(UDM) in addition to default.lib provided by EMTDC. We verify the correctness of developed TGOV5 model with PSS/E and EMTDC simulation results using Governor Step(GSTEP) method.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Wind and Structures
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• v.32 no.4
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• pp.371-391
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• 2021

Tornadoes are the most devastating meteorological natural hazards. Many empirical and theoretical numerical models of tornado vortex have been proposed, because it is difficult to carry out direct measurements of tornado velocity components. However, most of existing numerical models fail to explain the physical structure of tornado vortices. The present paper proposes a new empirical numerical model for a tornado vortex, and its load effects on a low-rise and a tall building are calculated and compared with those for existing numerical models. The velocity components of the proposed model show clear variations with radius and height, showing good agreement with the results of field measurements, wind tunnel experiments and computational fluid dynamics. Normal stresses in the columns of a low-rise building obtained from the proposed model show intermediate values when compared with those obtained from existing numerical models. Local forces on a tall building show clear variation with height and the largest local forces show similar values to most existing numerical models. Local forces increase with increasing turbulence intensity and are found to depend mainly on reference velocity Uref and moving velocity Umov. However, they collapse to one curve for the same normalized velocity Uref / Umov. The effects of reference radius and reference height are found to be small. Resultant fluctuating force of generalized forces obtained from the modified Rankine model is considered to be larger than those obtained from the proposed model. Fluctuating force increases as the integral length scale increases for the modified Rankine model, while they remain almost constant regardless of the integral length scale for the proposed model.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1998.04a
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• pp.188-195
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• 1998

The main objective of this study is to observe the effect of visco-elastic damper on seismic behavior of structure. A small shaking table was designed and manufactured using the materials and machinery available in the market by the authors. Also, two small-scale models with and without visco-elastic dampers were made. Dynamic characteristics, such as natural periods and damping ratios, were measured with free vibration tests. Finally, the earthquake simulations tests by using this shaking table were performed to these models. Based on the test results, the effect of visco-elastic damper on the seismic response of models is evaluated.

• Advances in concrete construction
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• v.1 no.4
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• pp.289-304
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• 2013

A reliable concrete constitutive material model is critical for an accurate numerical analysis simulation of reinforced concrete structures under extreme dynamic loadings including impact or blast. However, the formulation of concrete material model is challenging and entails numerous input parameters that must be obtained through experimentation. This paper presents a damage scale analytical model to characterize concrete material for its pre- and post-peak behavior. To formulate the damage scale model, statistical regression and finite element analysis models were developed leveraging twenty existing experimental data sets on concrete compressive strength. Subsequently, the proposed damage scale analytical model was implemented in the finite element analysis simulation of a reinforced concrete pier subjected to vehicle impact loading and the response were compared to available field test data to validate its accuracy. Field test and FEA results were in good agreement. The proposed analytical model was able to reliably predict the concrete behavior including its post-peak softening in the descending branch of the stress-strain curve. The proposed model also resulted in drastic reduction of number of input parameters required for LS-DYNA concrete material models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.928-941
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• 2016

Energy consumption of today's wired data networks is driven by access networks. Today, green networking has become a issue to reduce energy wastes and $CO_2$ emission by adding energy managing mechanism to wired data networks. However, energy consumption and environmental impacts of wired access networks are largely unknown. In addition, there is a lack of general and quantitative valuation basis of energy use of large-scale access networks and $CO_2$ emissions from them. This paper compared and analyzed limits of existing models estimating energy consumption of access networks and it proposed a model to estimate energy consumption of large-scale access networks by top-down approach. In addition, this work presented models that assess environmental(anti-greening) impacts of access networks using results from our models. The performance evaluation of the proposed models are achieved by comparing with previous models based on existing investigated materials and actual measured values in accordance with real cases.