• Tunnel and Underground Space
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• v.8 no.1
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• pp.17-25
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• 1998

This paper investigates coupled thermal, mechanical and hydraulic phenomena in deep rock mass especially for underground heat storage system. Firstly, concepts of underground heat storage were presented and coupling phenomena in this area were illustrated. In order to understand the basic mechanism of thermal, hydraulic and deformation behavior in rock cavern disturbed by thermal gradient about 10$0^{\circ}C$, various numerical experiments were conducted using several codes. The study involves the behavior of fractured rock mass including rock joint. In spite of the limitation of codes modelling fully coupled effects, these codes could be applied in analysis of underground heat storage. The heat loss in rock mass, which is a major factor in heat storage, is insignificant in all results.

• Tribology and Lubricants
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• v.13 no.4
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• pp.26-32
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• 1997

The heat generation of the angular contact and the deep groove ball bearing is studied experimentally and numerically. The temperature variation of the inner and outer races and the temperature increase distribution are measured for the shaft rotational speeds, preloads, viscosities of the lubricant and lubrication methods. The measured temperature distributions are used as the input data of the numerical simulation to estimate the heat generation rate at the bearing. The temperatures of the inner and outer race increase more rapidly and approach faster to their steady values as the rotational speed increases. The optimal viscosity of the oil to minimize the heat generation is 8~10 cSt at 4$0^{\circ}C$ when the oil-air lubrication method is adopted. The heat generation of the bearing increases with the rotational speed and depends more on the lubrication method than on the preload variation.

• 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 Broadcast Engineering
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• v.24 no.4
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• pp.564-572
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• 2019

With the development of deep learning, the performance of facial landmark detection methods has been greatly improved. The heat map regression method, which is a representative facial landmark detection method, is widely used as an efficient and robust method. However, the landmark coordinates cannot be directly obtained through a single network, and the accuracy is reduced in determining the landmark coordinates from the heat map. To solve these problems, we propose to combine integral regression with the existing heat map regression method. Through experiments using various datasets, we show that the proposed integral regression network significantly improves the performance of facial landmark detection.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.26-31
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• 2018

This paper introduces a study to improve the thermal efficiency of the district heating user control facility based on reinforcement learning. As an example, it is proposed a general method of constructing a deep Q learning network(DQN) using deep Q learning, which is a reinforcement learning algorithm that does not specify a model. In addition, it is also introduced the big data platform system and the integrated heat management system which are specialized in energy field applied in processing huge amount of data processing from IoT sensor installed in many thermal energy control facilities.

• Tunnel and Underground Space
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• v.21 no.6
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• pp.518-525
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• 2011

In this study, the natural ventilation pressure resulting from the large altitude difference which is a characteristic of high radioactive waste repository and the caloric value of the heat emitted by wastes was calculated and based on the results, natural ventilation quantities were calculated. A high radioactive waste repository can be considered as being operated through closed cycle thermodynamic processes similar to those of thermal engines. The heat produced by the heating of high radioactive wastes in the underground repository is added to the surrounding air, and the air goes up through the upcast vertical shaft due to the added heat while working on its surroundings. Part of the heat added by the work done by the air can be temporarily changed into mechanical energy to promote the air flow. Therefore, if a sustained and powerful heat source exists in the repository, the heat source will naturally enable continued cyclic flows of air. Based on this assumption, the quantity of natural ventilation made during the disposal of high radioactive wastes in a deep geological layer was mathematically calculated and based on the results, natural ventilation pressure of $74{\sim}183$Pa made by the stack effect was identified along with the resultant natural ventilation quantity of $92.5{\sim}147.7m^3/s$. The result of an analysis by CFD was $82{\sim}143m^3/s$ which was very similar to the results obtained by the mathematical method.

• Journal of Astronomy and Space Sciences
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• v.16 no.2
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• pp.177-190
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• 1999

An absorbed heat-flux method for ground simulation of on-orbit thermal environment of satellite is addressed in this paper. For satellite ground test, high vacuum and extremely low temperature of deep space are achieved by space simulation chamber, while spatial environmental heating is simulated by employing the absorbed heat-flux method. The methodology is explained in detail with test requirement and setup implemented on a satellite. Developed heat-load control system is presented with an adjusted PID-control logic and the system schematic realized is shown. A practical and successful application of the heat simulation method to KOMPSAT(Korea Multi-purpose Satellite)thermal environmental test is demonstrated, finally.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.12
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• pp.1784-1791
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• 2016

Garlic was subjected to eight different cooking methods (raw, boiling, steaming, microwave cooking, deep-frying, oven-roasting, pan-frying, and pan-roasting) utilized for typical Korean cuisine. Garlic was analyzed for antioxidant activities and physicochemical properties to elucidate effects of cooking. Garlic cooked at higher temperatures showed significantly lower lightness and higher yellowness (P<0.001). In particular, deep-frying and pan-frying resulted in lowest lightness and soluble solid content, indicating that non-enzymatic browning reactions were more facilitated. Compared with raw garlic, all cooked garlic tended to have lower thiosulfinates, presumably due to decomposition into polysulfides and/or leaching into cooking water and oil. Microwave cooking retained organic acids, total reducing capacity, and flavonoids, which can be attributed to low microwave intensity and shorter cooking time under which heat-labile bioactive components might have undergone less decomposition. Cooking significantly increased metal-chelating activity (P<0.001). In addition, oven-roasting and pan-roasting enhanced total reducing capacity and flavonoid content, indicating that thermal treatments increased the extractability of bioactive components from garlic. However, boiling, deep-frying, and pan-frying, in which garlic is in contact directly with a hot cooking medium, reduced antioxidant activities. Deep-frying resulted in largest reduction in DPPH radical scavenging activity of garlic, which correlated well with reduction of total reducing capacity and flavonoid content. The results show that the antioxidant activity of garlic could be affected by cooking method, particularly heat intensity and/or direct contact of the cooking medium.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.10
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• pp.93-98
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• 2019

Recently, with the development of the aerospace and automotive industries, the demand for high-melting-point materials has increased. However, high-melting-point materials are difficult to cut through conventional machining methods. Thermally assisted machining (TAM) is a method for improving the machinability by preheating the materials. A laser, the most commonly used device for TAM, has high efficiency through local preheating but is not sufficient for maintaining a high preheating temperature due to rapid cooling. However, the use of multi-heat sources can supplement the disadvantage of a single heat source. The high preheating temperature can be maintained with a wide and deep heat-affected zone (HAZ) by multi-heat sources. The purpose of this study is to analyze the preheating effects of multi-heat sources using laser plasma. Thermal analysis and preheating experiments were carried out. As a result, the high preheating effect of multi-heat sources compared with a single heat source was verified.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.