• 대한기계학회:학술대회논문집
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• 대한기계학회 2005년도 창립60주년 기념 추계 학술대회
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• pp.27.1-27.1
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• 2005

• International Journal of Concrete Structures and Materials
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• 제6권3호
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• pp.177-186
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• 2012

The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water-cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

• 방사성폐기물학회지
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• 제22권1호
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• pp.17-25
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• 2024

Hydraulic conductivity is a critical design parameter for buffers in high-level radioactive waste repositories. Most employed prediction models for hydraulic conductivity are limited to various types of bentonites, the main material of the buffer, and the associated temperature conditions. This study proposes the utilization of a novel integrated prediction model. The model is derived through theoretical and regression analyses and is applied to all types of compacted bentonites when the relationship between hydraulic conductivity and dry density for each compacted bentonite is known. The proposed model incorporates parameters such as permeability ratio, dynamic viscosity, and temperature coefficient to enable accurate prediction of hydraulic conductivity with temperature. Based on the results obtained, the values are in good agreement with the measured values for the selected bentonites, demonstrating the effectiveness of the proposed model. These results contribute to the analysis of the hydraulic behavior of the buffer with temperature during periods of high-level radioactive waste deposition.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3359-3366
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• 2021

An engineered barrier system (EBS) for the deep geological disposal of high-level radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling material, and backfill material. As the buffer fills the empty space between the disposal canisters and the near-field rock mass, heat energy from the canisters is released to the surrounding buffer material. It is vital that this heat energy is rapidly dissipated to the near-field rock mass, and thus the thermal conductivity of the buffer is a key parameter to consider when evaluating the safety of the overall disposal system. Therefore, to take into consideration the sizeable amount of heat being released from such canisters, this study investigated the thermal conductivity of Korean compacted bentonites and its variation within a temperature range of 25 ℃ to 80-90 ℃. As a result, thermal conductivity increased by 5-20% as the temperature increased. Furthermore, temperature had a greater effect under higher degrees of saturation and a lower impact under higher dry densities. This study also conducted a regression analysis with 147 sets of data to estimate the thermal conductivity of the compacted bentonite considering the initial dry density, water content, and variations in temperature. Furthermore, the Kriging method was adopted to establish an uncertainty metamodel of thermal conductivity to verify the regression model. The R² value of the regression model was 0.925, and the regression model and metamodel showed similar results.

• 한국분무공학회지
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• 제27권2호
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• pp.66-76
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• 2022

This study presents a prediction methodology of transport properties using the methane-based TRAPP (m-TRAPP) method in a wide range of temperature and pressure conditions including both subcritical and supercritical regions, in order to obtain thermo-physical properties for hydrocarbon aviation fuels and their products resulting from endothermic reactions. The viscosity and thermal conductivity are predicted in the temperature range from 300 to 1000 K and the pressure from 0.1 to 5.0 MPa, which includes all of the liquid, gas, and the supercitical regions of representative hydrocarbon fuels. The predicted values are compared with those data obtained from the NIST database. It was demonstrated that the m-TRAPP method can give reasonable predictions of both viscosity and thermal conductivity in the wide range of temperature and pressure conditions studied in this paper. However, there still exists large discrepancy between the current data and established values by NIST, especially for the liquid phase. Compared to the thermal conductivity predictions, the calculated viscosities are in better agreement with the NIST database. In order to consider a wide range of conditions, it is suggested to select an appropriate method through further comparison with another improved prediction methodologies of transport properties.

• 콘크리트학회논문집
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• 제13권4호
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• pp.305-313
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• 2001

본 연구에서는 콘크리트 열전도율의 영향인자에 대하여 TLPP원리를 응용한 QTM-D3 장비를 이용하여 실험을 실시하였고, 이들 실험결과를 이용하여 콘크리트의 열전도율을 예측하는 모델식을 제안하였다. 본 연구를 통하여 얻은 결론은 다음과 같다. 콘크리트, 모르타르 및 페이스트의 열전도율에 미치는 주요 영향인자를 구명하기 위해 본 연구에서 선택된 실험변수는 재령, 골재 함유량, 시멘트 함유량, 결합재 종류, 잔골재율, 시편의 온도 및 함수상태로 총 7가지이다. 이중에서 골재 함유량과 함수상태가 콘크리트 열전도율의 주요 영향인자임을 알 수 있었다. 그리고 시멘트 사용량이 많은 페이스트나 모르타르의 경우 시멘트 함유량이나 결합재 종류에 의해서도 열전도율이 영향을 받고 있다. 그러나 재령은 콘크리트의 열전도율에 큰 영향을 미치지 않음을 알 수 있었다. 콘크리트 열전도율에 주요 영향인자인 골재 함유량, 잔골재율, 시편의 온도 및 함수상태를 이용하여 콘크리트의 열전도율을 계산할 수 있는 모델식을 제안하였다.

• 한국도로학회논문집
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• 제16권4호
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• pp.87-95
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• 2014

PURPOSES: Evaluation of thermal conductivity and convection properties of asphalt mixture by using thermodynamics. METHODS: In this research, temperature prediction model based on thermodynamics is derived for asphalt mixture in transient state and it is verified with laboratory test results. RESULTS: The derived temperature prediction model shows good agreement with laboratory test results. CONCLUSIONS: It is concluded that the derived model based on thermodynamics and thermal properties in the literature are good enough to capture temperature variation in laboratory test. The approach based on thermodynamics can be applied to more complex temperature simulations.

• 한국태양에너지학회 논문집
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• 제26권4호
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• pp.1-7
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• 2006

This study is to develop a model to predict the soil temperature variation in Korea Institute of Energy Research using its thermal properties, such as thermal conductivity and diffusivity. Soil depth temperature variation is very important in the design of a proper Ground Source Heat Pump (GSHP) system. This is because the size of the borehole depends on the soil temperature distribution, and this can decrease GSHP system cost. If the thermal diffusivity and thermal conductivity are known, the soil temperature can be predicted by either the Krarti equation or the Spitler equation. Then a comparison with the Krarti equation and Spitler equation data with the real measured data can be performed. Also, the thermal properties can be reasonably approximated by performing a fit of the Krarti and Spitler equations with measured temperature data. This was done and, as a result, the Krarti equation and Spitler equation predicted values very close to the measured data. Although there is about a $0.5^{\circ}C$ difference between the deep subsurface prediction (16m - 60m), with this equation, were expected to have model this Non-Homogeneous Soil Temperature phenomenon properly. So, it has been shown that a prediction of non-homogeneous soil temperature variation influenced by solar radiation can be achieved with a model.

• 대한기계학회논문집B
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• 제34권2호
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• pp.153-156
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• 2010

나노유체로 기공이 채워진 규사와 같은 2상 물질의 고온에서의 유효 열전도도를 비정상열침법을 사용하여 측정하였다. 본 연구의 나노유체는 물과 0.1% 체적률의 입경이 45 nm 인 알루미나 나노입자의 혼합유체이다. 본 연구의 측정방법은 액체가 모래의 미세한 기공 내에 존재하므로, 열전도도의 측정에서의 액체의 대류에 의한 문제가 적다. 본 연구의 모래에 대한 예측모델을 사용하여 나노유체와 모래입자의 2상 물질의 유효 열전도도의 측정결과로부터, 고온의 나노유체의 열전도도를 결정하였다. 실험결과, $30^{\circ}C\sim80^{\circ}C$의 온도 범위에서 순수한 물에 대한 본 연구의 나노유체의 열전도도의 증가율은 4.87% ~ 5.48% 의 범위에서 변화하는 것으로 나타났다.

• 한국수산과학회지
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• 제15권3호
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• pp.219-225
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• 1982

1) 2성분계 도지두부의 유효열전도도($\lambda_e$)를 $0\sim20^{\circ}C$ 및$-5\sim-20^{\circ}C$의 범위내에서 측정한 결과, 수분함양의 증감에 따라 $\lambda_e$값도 증감했으며, 동결점이상의 온도대에서는 온도의 상승과 함께 $\lambda_e$값도 커지고, 동결점 이하에서는 온도의 하강과 함께 $\lambda_e$값이 커졌다. 2) 3성분계 두부의 유효열전도는 수분함양이 증가함에 따라 $\lambda_e$값은 커져가나 지질의 함양이 증가함에 따라 $\lambda_e$값은 작아졌다. $\lambda_e$값의 온도의존성은 2성분계도지두부의 경우와 유사했다. 3)대대단백질의 고유열전도도는 미동결상태에서 $\lambda_p=0.300[W/m{\bullet}k](0\sim-20^{\circ}C)$이고, 동결상태에서 $\lambda_p=0.488[W/m{\bullet}k](-5\sim-20^{\circ}C)$였으며 이들은 실험온도 범위내에서 온도의존성을 나타내지 않았다. 4) 대두단상질의 고유열전도도의 추정치 $\lambda_p=0.300$과 $\lambda_p=0.488$의 타당성을 지질의 함양이 상이한 2종류의 3성분계 두부에 적용한 결과, 실측치와 계산치는 잘 일치했다.