• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.260-266
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• 2001

The surface temperature at the interface of bodies in a sliding contact is one of the most important factors influencing the behavior of machine components. So the calculation of the surface temperature at a sliding contact interface has long been an interesting and important subject for tribologist. Several methods for calculating surface temperature have been devised. Several numerical methods have been used to predict the temperature rise of sliding surface. but those need much time to calculate. In this study to reduce the calculation time the hybrid method using both semi-infinite solid analysis and FVM was used. It is founded that the computing time of hybrid method was shorter than that of FVM.

• Journal of The Korean Astronomical Society
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• v.39 no.4
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• pp.125-128
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• 2006

In order to determine the precise effective temperature and surface gravity of warm stars, all synthetic spectral lines in the wavelength range of $4000-5700{\AA}$ with T=6000-7750 K, and log g=3.5, 4.0, and 4.5 for [M/H]=0.0, $V_{rot}$=10 km $s^{-1}$, and $V_{tubl}$=2 km $s^{-1}$ were calculated using the SYNSPEC package(Hubeny, et al., 1995) and the Kurucz(1995) model. Then, the depth-ratios for all line pairs were investigated and we selected two and six depth-ratios appropriate for the surface gravity and temperature indicators, respectively. We plotted six grids with X- and Y-axes for the depth-ratios of surface gravity and temperature, respectively, for the simultaneous estimation of these two atmospheric parameters. This method was applied to the spectum of $\delta$ Scu for the determination of its temperature and surface gravity simultaneously.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.643-650
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• 2002

It is necessary, in the light of the importance of long-term slope stability problem, to develop a simple method or tool which can figure out the possible failure zone resulted from weathering effect and other factors. The FBG sensor system is used to estimate the correlations between the temperature and the slope in Yunhwajae, and to find a failure zone in slopes effectively. This research is to seek for the correlation between the soil temperature distribution and the strain distribution in a active zone by analyzing the data from the in-situ measurement so that the possible failure zone should be well defined based on the correlation. The zone of high temperature fluctuation can be regarded as one of the possible sliding zone due to the weathering effect while the constant temperature depth of the ground, if exists, would not be relatively affected by the weathering process.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.6
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• pp.393-400
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• 2017

For comparison to the Li-ion battery, evaluating a thermal battery must consider additional variables. The first one is the temperature difference between the battery and its unit cell. Thermal batteries and their unit cells have a temperature difference that is caused by the thermal battery activation mechanism and its shape. The second variable is the electrochemical reaction steps. Most Li-ion batteries have a constant electrochemical reaction at the electrode, and battery voltage is affected when the concentration of Li ions is changed. However, a thermal battery has several steps in its electrochemical reaction, and each step has a different potential. In this study, we used unit cell discharge tests based on interpolating a 4D lookup table to estimate the performance of a thermal battery. From the test results, we derived an estimation algorithm by interpolating the table, which is queried from specified profile groups. As a result, we found less than a 5 percent difference between estimation and experiment at the 1.3 V cut-off time.

• Journal of IKEEE
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• v.26 no.1
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• pp.10-18
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• 2022

Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.

• Journal of the Korean Society for Advanced Composite Structures
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• v.6 no.3
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• pp.32-40
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• 2015

The torque shear high strength bolt is clamped normally at the break of pin-tail specified. However, the clamping forces on slip critical connections do not often meet the required tension, as it considerably fluctuates due to torque coefficient dependent on lubricant affected temperature. In this study, the clamping tests of torque shear bolts were conducted independently at indoor conditions and at construction site conditions. During last six years, temperature of candidated site conditions was recorded from $-11^{\circ}C$ to $34^{\circ}C$. The indoor temperature condition was ranged from $-10^{\circ}C$ to $50^{\circ}C$ at each $10^{\circ}C$ interval. As for site conditions, the clamping force was reached in the range from 159 to 210 kN and the torque value was from 405 to $556 N{\cdot}m$. The range of torque coefficient at indoor conditions was analyzed from 0.126 to 0.158 while tensions were indicated from 179 to 192 kN. The torque coefficient at site conditions was ranged from 0.118 to 0.152. Based on this test, the variable trends of torque coefficient, tension subjected temperature can be taken by statistic regressive analysis. The variable of torque coefficient under the indoor conditions is $0.13%/^{\circ}C$ while it reaches $2.73%/^{\circ}C$ at actual site conditions. When the indoor trends and site conditions is combined, the modified variable of torque coefficient can be expected as $0.2%/^{\circ}C$. and the modified variable of tension can be determined as $0.18%/^{\circ}C$.

• Journal of Hydrogen and New Energy
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• v.31 no.5
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• pp.489-497
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• 2020

The Li-ion battery is considered to be one of the potential power sources for electric vehicles. In fact, the efficiency, reliability, and cycle life of Li-ion batteries are highly influenced by their thermal conditions. Therefore, a novel thermal management system is highly required to simultaneously achieve high performance and long life of the battery pack. Basically, thermal modeling is a key issue for the novel thermal management of Li-ion battery systems. In this paper, as a basic study for battery thermal modeling, temperature distributions inside the simple Li-ion battery pack (comprises of nine 18650 Li-ion batteries) under a 1C discharging condition were investigated using measurement and computational fluid dynamics (CFD) simulation approaches. The heat flux boundary conditions of battery cells for the CFD thermal analysis of battery pack were provided by the measurement of single battery cell temperature. The temperature distribution inside the battery pack were compared at six monitoring locations. Results show that the accurate estimation of heat flux at the surface of single cylindrical battery is paramount to the prediction of temperature distributions inside the Li-ion battery under various discharging conditions (C-rates). It is considered that the research approach for the estimation of temperature distribution used in this study can be used as a basic tool to understand the thermal behavior of Li-ion battery pack for the construction of effective battery thermal management systems.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.56-61
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• 2021

This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

• Journal of Environmental Health Sciences
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• v.7 no.1
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• pp.9-20
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• 1981

This study was performed to determine the correlationship between temperature and overall removals of BOD, SS and to demonstrate the effect of temperature on treatment performance. These data for a period from February 1, 1977 to January 31, 1980 were obtained from the Cheong-Gye Cheon Sewage Treatment plant. The results of correlation and stepwise multiple regression analysis were as follows. 1) Secondary effluent BOD and SS showed negative correlationship with water temperature, with correlation coefficient of -0.1710, and -0.1654 respectively. 2) Correlation coefficient of BOD, SS removal rate and water temperature were 0.1823 and 0.0429 respectively. 3) Regresion equation for estimate of BOD removal rate was as follows $\widehat{Y}_1$ (BOD removal rate)=63.9994+0.5442X(water temperature). And BOD removal rate showed non significant change according to the water temperature. 4) Regression equation for estimate of SS removal rate was as follows $\widehat{Y}_2$ (SS removal rate)=61.6881+0.1514X(Water temperature). And SS removal rate showed non significant change according to the water temperature. 5) According to the Stepwise Multiple Regression analysis, water temperature ranked second order in the BOD removal rate estimation and the equation was as follows $\widehat{Y}_1$ (BOD removal rate)=69.7398+0.2665 $X_1$ (Primary effluent BOD)+0.3562 $X_2$ (Water temperature)-0.0122 $X_3(Flow)+4413.271X_4$ (Organic Loading).