• Journal of Adhesion and Interface
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• v.22 no.1
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• pp.1-7
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• 2021

The importance of high capacity energy storage devices has recently emerged for stable power supply through renewable energy generation. From this point of view, the Na-air battery (NAB), which is a next-generation secondary battery, is receiving huge attention because it can realize a high capacity through abundant and inexpensive raw materials. In this study, activated carbon-based catalysts for hybrid type Na-air batteries were prepared and their characteristics were compared and analysed. In particular, from the viewpoint of resource recycling, activated carbon (Orange-C) was prepared using discarded orange peel, and performance was compared with Vulcan carbon, which is widely used. In addition, a Pt/C catalyst (homemade-Pt/C, HM-Pt/C) was synthesized using a modified polyol method to check whether the prepared activated carbon can be used as a supported catalyst, and a commercial Pt/C catalyst (Commercial Pt/C) and electrochemical performance were compared. The prepared Orange-C exhibited a typical H3 type BET isotherm, which is evidence that micropore and mesopore exist. In addition, in the case of HM-Pt/C, it was confirmed through TEM analysis that Pt particles were evenly distributed on the activated carbon supported catalyst. In particular, the HM-Pt/C-based NAB showed the smallest voltage gap (0.224V) and good voltage efficiency (92.34%) in the 1st galvanostatic charge-discharge test. In addition, the cycle performance test conducted for 20 cycles showed the most stable performance.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.69-69
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• 2011

태양열 발전 플랜트에 사용되는 중고온 범위의 축열조에 고체-액체간 상변화를 수행하는 용융염을 축열물질로 사용하면 액체상 또는 고체상만으로 된 열저장 매체에 비해 축열조의 규모를 축소함과 동시에 축열온도의 균일성 향상에 기여할 수 있다. 중온인 $250{\sim}400^{\circ}C$ 범위에서 이용 가능한 용융염으로는 질산칼륨($KNO_3$), 질산리튬($LiNO_3$)등이 있다. 그러나 이러한 용융염의 가장 큰 단점은 열전도율이 매우 낮다는 것이며, 이로 인해 요구되는 열전달률을 성취하기 위해서는 많은 열접촉면적이 필요하다는 것이다. 이러한 단점을 극복하는 방법을 도입하지 않고서는 축열시스템의 소규화를 성취하는데 큰 효과를 가져올 수 없다. 한편 열수송 성능이 탁월한 히트파이프를 사용하면 열원 및 열침과 축열물질 사이의 열전달 효율을 증가시켜 시스템의 성능 향상과 동시에 소규모화에 기여할 수 있다. 중온 범위 히트파이프의 작동유체로서 다우섬-A(Dowtherm-A)는 $150^{\circ}C$이상 $400^{\circ}C$까지의 범위에서 소수에 불과한 선택적 대안 중 하나이다. 따라서 본 연구에서는 용융염을 사용하는 중온 태양열축열조에 적용 가능한 다우섬-A 히트파이프의 성능을 파악하여 기술적 자료를 제시하고자 하였다. 열원으로는 고온 고압의 과열증기, 그리고 열침으로는 중온의 포화증기를 고려하였다. 용융염 축열조를 수직으로 관통하는 히트파이프는 하단부에서 열원 증기와 열교환 가능하며, 중앙부에서 축열물질과 열교환하고, 상단부에서는 중온 증기와 접촉할 수 있도록 배치하였다. 축열모드에서는 히트파이프의 하단부가 증발부로 작동하고, 중앙부가 응축부로 작동하여 용융염으로 열을 방출하면 용융염의 온도가 상승하고 용융점에 도달하면 액상으로의 상변화가 진행되면서 축열이 활성화된다. 축열모드에서 히트파이프의 상단부는 단열부로 작동한다. 방열과정에서는 히트파이프의 하단부가 단열된 상태이고, 중앙부는 용융염으로부터 열을 받아 증발부로 작동하며, 상단부는 중온 증기로 열을 방출하므로 응축부로 작동한다. 즉, 축열시스템의 작동모드에 따라 하나의 히트파이프에서 증발부, 응축부, 단열부의 위치가 변하게 된다. 특히, 히트파이프의 중앙 부분이 응축부에서 증발부로 전환될 때에도 작동이 보장되려면 내부 작동유체의 연속적인 재순환이 가능해야 하므로, 일반 히트파이프에서와는 달리 초기 작동액체의 충전량을 증발부 전체의 체적보다 더 많이 과충전해야 한다. 이러한 히트파이프의 성능 파악을 위한 실험에서 고려한 변수들은 열부하, 작동액체의 충전률, 작동온도 등이며, 열수송 성능의 지표로서는 유효열전도율과 열저항을 이용하였다. 중온범위에서 적정한 작동온도를 성취하기 위해 실험에서는 전압 조절기로 열부하를 조절하는 동시에 항온조로 응축부의 냉각수 입구 온도를 제어하였다. 하나의 히트파이프에 대해서 최대 1 kW까지의 열부하에서 냉각수 입구 온도를 $40^{\circ}C$에서 $80^{\circ}C$ 범위로 변화시키면 히트파이프 작동온도를 약 $250^{\circ}C$ 내외로 조절 가능하였다. 히트파이프 작동액체 충전률은 윅구조물의 공극 체적을 기준으로 372%에서 420%까지 변화 시켰다. 실험 결과를 토대로 열저항과 유효 열전도율을 각각 입력 열유속, 작동온도, 작동액체 충전률 등의 함수로 제시했다. 동일한 냉각수 온도에서는 충전률이 높을수록 히트파이프의 작동온도가 감소하였다. 열저항 값의 범위는 최소 $0.12^{\circ}C/W$에서 최대 $0.15^{\circ}C/W$까지로 나타났으며 유효 열전도율의 값은 최소 $7,703W/m{\cdot}K$에서 최대 $8,890W/m{\cdot}K$까지 변화했다. 최소 열저항은 충전률 420%인 경우에 나타났는데 이때의 작동온도는 약 $262^{\circ}C$이었다. 히트파이프의 작동한계로서 드라이아웃(dry-out)은 충전률 372%의 경우에 열부하 950 W에서 발생하였으나, 그 이상의 충전률에서는 열부하 1060 W까지 작동한계 발생이 관찰되지 않았다. 실험 결과 본 연구에서의 히트파이프는 중온 태양열 축열조에 적용되어 개당 약 1 kW의 열부하를 이송하면서 축열물질 및 축방열 대상 유동매체와 열교환을 하는데 사용하는데 충분할 것이라 판단된다.

• Journal of Environmental Policy
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• v.10 no.3
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• pp.21-48
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• 2011

The paper affirms the weight proportion concept as an element, which could be empirically analyzed quantitatively through the developmental expansion of qualitative empirical analysis on taxonomy that is required for the construction Low-Carbon Green Cities and their eco-friendly elements, such as a pleasant residential environment The weight proportion concept is proposed as a new measure to identify eco-friendly elements and as an objective assessment indicators. To perform an empirical analysis, surveys were first given to the residents of Metropolitan Newtown (50 persons) and outside experts (50 persons) for the total of 100 persons. Second, the paper surveyed 74 sites of Eco-Environmental Certified Apartment Housing Complexes. Upon analysis of eco-friendly elements by their type and total weight, the largest weight proportion was expressed in the interior and eco-environmental elements, carbon-decrease types, and carbon-absorption types. The results of this paper confirm recent positive sentiments and preference toward a variety of future-oriented and sustainable eco-friendly elements like the eco-housing and new renewable energy, In addition, the paper affirmed the new housing trend towards constructing eco-friendly elements, such as carbon-decrease and carbon-absorption, that induce long-term investments, despite their need for higher investments.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.6
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• pp.52-59
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• 2010

As electric power demand of customers is constantly increasing, more bulk power systems are needed to install in a network. By development of renewable energies and high-efficient facilities and deregulated electricity market, moreover, the amount of distributed resource is considerably increasing in distribution network consequently. Also, distribution network has become more and more complex as mesh network to improve the distribution system reliability and increase the flexibility and agility of network operation. These changes make fault current increase. Therefore, the fault current will exceed a circuit breaker capacity. In order to solve this problem, replacing breaker, changing operation mode of system and rectifying transformer parameters can be taken into account. The SFCL(Superconducting Fault Current Limiter) is one of the most promising power apparatus. This paper proposes a methodology for on optimal location of SFCL. This place is defined as considering the decrement of fault current by component type and the increment of reliability by customer type according to an location of SFCL in a distribution network connected with DG(Distributed Generation). With case studies on method of determining optimal location for SFCL applied to a radial network and a mesh network respectively, we proved that the proposed method is feasible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.80-87
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• 2018

In recent years, due to the spread of various renewable energy power sources and the pursuit of high efficiency and low-power consumption, not only trends in the electric power industry but also the consumption, control methods, and characteristics are diversified. However, in this diversified electric power industry, the fuse (which is the core part responsible for safety) has not developed significantly in classical operation mode, and thus, fires continue to occur. In this paper, the effects of low melting-point metal plating and high melting-point metal plating on operating characteristics and IT curve movement of the fuse are investigated in a cartridge fuse, which is a classic fuse manufacturing method. The effects of plating on the thickness of the fuse are investigated, and various operating characteristics of the fuse are implemented. In addition, it is suggested that the plating of the low melting-point metal moves the rated current line of the fuse to a low rating, and moves operating characteristics to characteristics of delay operation. It is possible to design various operating characteristics using this characteristic.

• Journal of Korea Port Economic Association
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• v.28 no.3
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• pp.27-44
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• 2012

The offshore wind farm is increasingly attractive as one of future energy sources all over the world. In addition, the capacity of an offshore wind turbine gets larger and its physical characteristics are big and heavy. In this regard, a special port is necessary to assemble, store, and transport the offshore wind systems, supporting to form the offshore wind farms. Thus, this study aims to provide a policy maker which evaluation factors can significantly affect to the optimal site selection of a offshore wind port. For this, Fuzzy-AHP method is applied to capture the relative weights. The results of this study can be summarized as follows. Five criteria in level I was defined such as the accumulation factor, the regional factor, the economic factor, the location factor, and the consortium factor. Of these, the accumulation factor(37.4%), the location factor(34.2%), and the economic factor( 24.5%) were analyzed by major factors. In level II, three assessment items of each factor were selected so that total fifteen items were formed. To sum up, the site selection of offshore wind port should consider the density of the wind industry, cargo volume of securing the economic operation of terminals, the development degree of offshore wind related industry, and the proximity to the offshore wind farms. In other words, the construction of offshore wind port should be paid attention to considering not only the proximity to offshore wind farms but also the preference of turbine manufacturing companies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.846-855
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• 2021

Eco-friendly and renewable energy sources are actively being researched in recent times, and of shore wind power generation requires advanced design technologies in terms of increasing the capacities of wind turbines and enlarging wind turbine installation vessels (WTIVs). The WTIV ensures that the hull is situated at a height that is not affected by waves. The most important part of the WTIV is the leg structure, which must respond dynamically according to the wave, current, and wind loads. In particular, the wave load is composed of irregular waves, and it is important to know the exact dynamic response. The dynamic response analysis uses a single degree of freedom (SDOF) method, which is a simplified approach, but it is limited owing to the consideration of random waves. Therefore, in industrial practice, the time-domain analysis of random waves is based on the multi degree of freedom (MDOF) method. Although the MDOF method provides high-precision results, its data convergence is sensitive and difficult to apply owing to design complexity. Therefore, a dynamic amplification factor (DAF) estimation formula is developed in this study to express the dynamic response characteristics of random waves through time-domain analysis based on different variables. It is confirmed that the calculation time can be shortened and accuracy enhanced compared to existing MDOF methods. The developed formula will be used in the initial design of WTIVs and similar structures.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.