• Proceedings of the KSME Conference
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• 2005.11a
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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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• v.6 no.3
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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.

• Nuclear Engineering and Technology
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• v.53 no.10
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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.

• Journal of ILASS-Korea
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• v.27 no.2
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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.

• Journal of the Korea Concrete Institute
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• v.13 no.4
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• pp.305-313
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• 2001

Conductivity is an important thermal property which governs heat transfer in a solid medium. Generally, the determination of conductivity in concrete is very difficult, because concrete is a heterogeneous material composed of cement, water, aggregate, et cetera and time dependent material of which properties change with curing age. In this study, influencing factors on thermal conductivity of concrete are quantitatively investigated by QTM-D3, a conductivity tester developed in Japan. Then, a prediction equation of thermal conductivity of concrete is suggested from the regression analysis of test results. To consider the factors influencing thermal conductivity of concrete, mortar, and cement paste, seven testing variables (age, amount of cement, types of admixtures, amount of coarse aggregate, fine aggregate ratio, temperature, and humidity condition) of the specimens are used. According to the experimental results, the amount of coarse aggregate and humidity condition of specimen are the main factors affecting the conductivity of concrete. Meanwhile, the conductivity of mortar and cement paste is strongly affected by the amount of cement and types of admixtures. However, the curing age has minor effect on the conductivity variation. Finally, the prediction formula of concrete conductivity as a function of aggregate amount, fine aggregate ratio, specimen temperature, and humidity condition is developed.

• International Journal of Highway Engineering
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• v.16 no.4
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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.

• Journal of the Korean Solar Energy Society
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• v.26 no.4
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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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.2
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• pp.153-156
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• 2010

The effective thermal conductivity of two-phase materials such as unbonded silica sands saturated with a nanofluid was measured at high temperature using the transient thermal probe method. The nanofluid used in this study was a water-based mixture of 0.1 vol% $Al_2O_3$ nanoparticles with a diameter of 45 nm. The convection problem for fluids was prevented with this measurement method because the fluid was confined to within very small pore spaces. Based on the prediction model for unbonded sands, the thermal conductivities of the saturating nanofluid at high temperatures could be determined with the measured effective thermal conductivities for the two-phase material. In the results, increases in the thermal conductivity ratios of the nanofluid to pure water when temperatures were varied from $30^{\circ}$ to $80^{\circ}C$ were within the range of 4.87%~5.48%.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.15 no.3
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• pp.219-225
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• 1982

Four heat conduction models were examined for defatted soy-protein curds in order to get the 'intrinsic' thermal conductivity of soy-protein. As the result of examination, the 'intrinsic', thermal conductivities of soy-protein, frozen and unfrozen states, were determined on the basis of series model to be 0.488 W/m.K and 0.300 W/m.K, respectively. By using the 'intrinsic' thermal conductivity values of soybean protein and the series model, the effective thermal conductivity of soybean curds, with and without fat, at frozen and unfrozen states, was predicted satisfactorily, The temperature dependency of the effective thermal conductivity of soybean curd was mostly observed to correlate with the thermal conductivity of water and ice.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.5
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• pp.213-219
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• 2017

This study investigated the thermal conductivity of $CO_2$ gas mixtures in order to ascertain the effects of particular impurities in $CO_2$ in pipeline transportation. We predicted the thermal conductivity of three $CO_2$ gas mixtures ($CO_2+N_2$, $CO_2+H_2S$, and $CO_2+CH_4$) by utilizing three different methods : Chung et al., TRAPP, and the REFPROP model. We validated predictions by comparing the estimated results with 216 experimental data for $CO_2+CH_4$, $CO_2+N_2$, and $CO_2+C_2H_6$. Following $CO_2$ transportation conditions, we observed that the model developed by Chung et al. showed the lowest mean deviation of 3.07%. Further investigations were carried out on the thermal conductivity of $CO_2$ gas mixtures based on the Chung et al. model including the effects of the operation parameters of pressure, temperature, and mole fraction of impurities.