• Title/Summary/Keyword: Industrial cooling technology

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Study on tension-tension fatigue strength properties of underwater welded joints of SM41A-2 Plate-to-Plate (수중용접한 국산 SM41A-2강판의 편진반복 인장하중하의 피로강도특성에 관한 연구)

  • 오세규;박주성;한상덕
    • Journal of Advanced Marine Engineering and Technology
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    • v.11 no.2
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    • pp.71-81
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    • 1987
  • Nowadays, the high development of industrial technique demands the optimal design of marine structures to be welded under the water, because the underwater welding of the ship hull and marine structures can decrease manpower and cost of production. However there is not available at present any report on fatigue behavior about underwater welded joints. In this paper under tention- tension repeated fatigue stress with frequency of 10 cycles per second by local controlled system, the fatigue strength properties of underwater welded joints of SM41A-2 Plate-to-Plate of 10 mm thickness were experimentally examined. The results obtained were as follows : 1) The fatigue strength of underwater welded joints of SM41A-2 was peaked at the heat input of about 1, 400 joule/mm(180 A, 36 V), while, at the heat input of more than about 1, 100 joule/mm (160 A, 33 V) that of the underwater welds at the higher than cycle of life rather than the lower cycle was higher than that of the base metal but lower than that of the atmosphere welds on account of both cooling and notch effects. 2) The fatigue limit of underwater welds increased with an increase of heat input resulting in a peak of that at the heat input of about 1, 400 joule/mm and then decreased gradually. 3) The fatigue strength at N cycles was peaked between the heat input of about 1, 400 and 1, 700 joule/mm where the strain was rapidly increased. 4) It was confirmed that the optimal zone of heat input condition for obtaining the underwater welds fatigue strength higher than that of the base metal exists, and if out of this zone, the fatigue strength of the underwater welds was lower than that of the base metal because of lack weld penetration, inclusion of slag, voids, etc. 5) By the fatigue test, the underwater welds fractured brittly without visual deformation, so the strain was remarkably less than of the atmosphere welds. 6) The fatigue life factor was peaked at the heat input of about 1, 600 joule/mm (200 A, 36 V) at which the mean strain is a little higher than that of the base metal but quite lower than those of the atmosphere welds, resulting in good underwater welds because both fatigue strength and ductility of the underwater welds are higher than those of the base metal at such heat input.

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Influence of $TiO_2$ on Sintering and Microstructure of Magnesia-Zirconia Composites (마그네시아 지르코니아 복합소결체의 소결과 미세구조에 미치는 $TiO_2$의 영향)

  • Lee, Yun-Bok;Kim, In-Sul;Jang, Yun-Sik;Park, Hong-Chae;O, Gi-Dong
    • Korean Journal of Materials Research
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    • v.4 no.7
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    • pp.775-782
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    • 1994
  • Influence of $TiO_{2}$ addition on sintering behavior and microstructure of MgO-$ZrO_{2}$ composites was studied. $ZrO_{2}$ containing 3mol%Y203 was existed as a c-$ZrO_{2}$ phase due to the formation of solubility of MgO, $TiO_{2}$ and $ZrO_{2}$ when sintered $1400^{\circ}C$ for 2h. All the compositions employed exhibited a similar shrinkage behavior with an end-point shrinkage between 8.58 and 11.00%. The additlon of $TiO_{2}$ promoted densification and the bulk density of specimen containing 1.67wt% $TiO_{2}$ was 3.75g/$\textrm{cm}^3$(98% TD) when $1600^{\circ}C$ for 2h. The amount of solubilities of MgO and TiOz in $ZrO_{2}$ were 5.ti7wt% and 2.62wt%,respectively. They were partially segregated near $ZrO_{2}$ grain boundary in the form of Ti-compounds during cooling. This segregation resulted in the formation microcracks which decreased the bending strength.

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0.1 MW Test Bed CO2 Capture Studies with New Absorbent (KoSol-5) (신 흡수제(KoSol-5)를 적용한 0.1 MW급 Test Bed CO2 포집 성능시험)

  • Lee, Junghyun;Kim, Beom-Ju;Shin, Su Hyun;kwak, No-Sang;Lee, Dong Woog;Lee, Ji Hyun;Shim, Jae-Goo
    • Applied Chemistry for Engineering
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    • v.27 no.4
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    • pp.391-396
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    • 2016
  • The absorption efficiency of amine $CO_2$ absorbent (KoSol-5) developed by KEPCO research institute was evaluated using a 0.1 MW test bed. The performance of post-combustion technology to capture two tons of $CO_2$ per day from a slipstream of the flue gas from a 500 MW coal-fired power station was first confirmed in Korea. Also the analysis of the absorbent regeneration energy was conducted to suggest the reliable data for the KoSol-5 absorbent performance. And we tested energy reduction effects by improving the absorption tower inter-cooling system. Overall results showed that the $CO_2$ removal rate met the technical guideline ($CO_2$ removal rate : 90%) suggested by IEA-GHG. Also the regeneration energy of the KoSol-5 showed about $3.05GJ/tonCO_2$ which was about 25% reduction in the regeneration energy compared to that of using the commercial absorbent MEA (Monoethanolamine). Based on current experiments, the KoSol-5 absorbent showed high efficiency for $CO_2$ capture. It is expected that the application of KoSol-5 to commercial scale $CO_2$ capture plants could dramatically reduce $CO_2$ capture costs.

Development of a complex failure prediction system using Hierarchical Attention Network (Hierarchical Attention Network를 이용한 복합 장애 발생 예측 시스템 개발)

  • Park, Youngchan;An, Sangjun;Kim, Mintae;Kim, Wooju
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.127-148
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    • 2020
  • The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.