• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.8 no.4
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• pp.319-327
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• 2010

A nuclear plant ESF ACS simulator was designed, built, and verified to perform experiment related to ESF ACS of nuclear power plants. The dimension of 3D CAD model was based on drawings of the main control room(MCR) of Yonggwang units 5 and 6. The CFD analysis was performed based on the measurement of the actual flow rate of ESF ACS. The air flowing in ACS was assumed to have $30^{\circ}C$ and uniform flow. The flow rate across the HEPA filter was estimated to be 1.83 m/s based on the MCR ACS flow rate of 12,986 CFM and HEPA filter area of 9 filters having effective area of $610{\times}610mm^2$ each. When MCR ACS was modeled, air flow blocking filter frames were considered for better simulation of the real ACS. In CFD analysis, the air flow rate in the lower part of the active carbon adsorber was simulated separately at higher than 7 m/s to reflect the measured value of 8 m/s. Through the CFD analyses of the ACSes of fuel building emergency ventilation system, emergency core cooling system equipment room ventilation cleanup system, it was confirmed that all three EFS ACSes can be simulated by controlling the flow rate of the simulator. After the CFD analysis, the simulator was built in nuclear grade and its reliability was verified through air flow distribution tests before it was used in main tests. The verification result showed that distribution of the internal flow was uniform except near the filter frames when medium filter was installed. The simulator was used in the tests to confirm the revised contents in Reg. Guide 1.52 (Rev. 3).

• Economic and Environmental Geology
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• v.48 no.1
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• pp.25-39
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• 2015

$CO_2$ geological storage plays an important role in reduction of greenhouse gas emissions, but there is a lack of research for CCS demonstration. To achieve the goal of CCS, storing $CO_2$ safely and permanently in underground geological formations, it is essential to understand the characteristics of them, such as total storage capacity, stability, etc. and establish an injection strategy. We perform the impedance inversion for the seismic data acquired from the Ulleung Basin in 2012. To review the possibility of $CO_2$ storage, we also construct porosity models and extract attributes of the prospects from the seismic data. To improve the quality of seismic data, amplitude preserved processing methods, SWD(Shallow Water Demultiple), SRME(Surface Related Multiple Elimination) and Radon Demultiple, are applied. Three well log data are also analysed, and the log correlations of each well are 0.648, 0.574 and 0.342, respectively. All wells are used in building the low-frequency model to generate more robust initial model. Simultaneous pre-stack inversion is performed on all of the 2D profiles and inverted P-impedance, S-impedance and Vp/Vs ratio are generated from the inversion process. With the porosity profiles generated from the seismic inversion process, the porous and non-porous zones can be identified for the purpose of the $CO_2$ sequestration initiative. More detailed characterization of the geological storage and the simulation of $CO_2$ migration might be an essential for the CCS demonstration.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.5
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• pp.66-77
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• 2019

This study investigated the feasibility of wintering evergreen broad-leaf trees in the Incheon coastal area through a climate analysis. The coldest monthly mean air temperature ranged from $-2.9^{\circ}C{\sim}-1.6^{\circ}C$. The warmth index of the coastal area of Incheon ranged from $98.89^{\circ}C{\cdot}month-109.03^{\circ}C{\cdot}month$, while the minimum air temperature year ranged from $-13.9^{\circ}C{\sim}-3.6^{\circ}C$. This proved that the Incheon coastal area was not suitable for evergreen broad-leaf trees to grow as the warmth index ranges from $101.0^{\circ}C{\cdot}month{\sim}117.0^{\circ}C{\cdot}month$, and the temperature year-round is $-9.2^{\circ}C$ or higher. This suggests the coastal areas of Incheon is not suitable for the growth of evergreen broad-leaf trees, however some evergreen broad-leaf trees lived in some parts of the area. Wind speed reduction and temperature effect simulations were done using Landschaftsanalyse mit GIS program. As a result of the simulations of wind speed reduction and temperature effects affecting the evergreen broad-leaf trees, it was discovered that a coastal wind velocity of 8.6m/sec was alleviated to be 5m/sec~7m/sec when the wind reached the areas where evergreen broad-leaf trees were present. It was also discovered that species that grew in contact with buildings benefited from a temperature increase of $1.1^{\circ}C{\sim}3.4^{\circ}C$ due to the radiant heat released by the building. Simulation results show that the weather factors affecting the winter growth damages of evergreen broad-leaved trees were wind speed reduction and local warming due to buildings. The wind speed reduction by shielding and local warming effects by buildings have enabled the wintering of evergreen broad-leaved trees. Also, evergreen broad-leaved trees growing in the coastal area of Incheon could be judged to be gradually adapting to low temperatures in winter. This study reached the conclusion that the blockage of wind, and the proximity of buildings, are required for successfully wintering evergreen broad-leaf trees in the coastal area of Incheon.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.5
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• pp.13-29
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• 2023

Urban parks are social infrastructure supporting citizens' health, quality of life, and community formation. As the proportion of urban parks that have been established for more than 20 years is increasing, the need for refurbishment to improve the physical space environment and enhance the functions of aging urban parks is increasing. Since the government's refurbishment of aging urban parks has limitations in securing financial resources and promoting attractiveness, they must be promoted through public-private partnerships. Japan, which suffered from the problem of aging urban parks, has successfully promoted several park refurbishment projects by introducing the Park-PFI through the revision of the 「Urban Park Act」 in 2017. This study examines and analyzes the characteristics of the Japan Park-PFI as an alternative to improving the quality of aging domestic urban park services through public-private partnerships and the validity of the aging urban park refurbishment projects through Park-PFI. The main findings are as follows. First, it is necessary to start discussions on introducing Japan's Park-PFI according to the domestic conditions as a means of public-private partnership to improve the service quality and diversify the functions of aging urban parks. In order to introduce Park-PFI social discussions and follow-up studies on the deterioration of urban parks. Must be conducted. The installation of private capital and profit facilities and improvements of related regulations, such as the 「Parks and Green Spaces Act」 and the 「Public Property Act」, is required. Second, it is judged that the Park-PFI project is a policy alternative that can enhance the benefits to citizens, local governments, and private operators under the premise that the need to refurbish aging urban parks is high and the location is suitable for promoting the project. As a result of a pilot application of the Park-PFI project to Seyeong Park, an aging urban park located in Bupyeong-gu, Incheon, it was analyzed to be profitable in terms of the profitability index (PI), net present value (FNPV), and internal rate of return (FIRR). It is considered possible to participate in the business sector. At the local government level, private capital is used to improve the physical space environment of aging urban parks, as well as the refurbishment of the urban parks by utilizing financial resources generated by returning a portion of the facility usage fees and profits (0.5% of annual sales) of private operators. It was found that management budgets could be secured.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.53-62
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• 2023

This paper has suggested the necessity of introducing training contents using XR(Extended reality) technology as a way to lower the high rate of nursing accidents among unskilled technical personnel in domestic shipbuilding industry, focusing on cable pulling using winch. The occurrence rate of nursing accidents in the domestic shipbuilding industry was almost double(197.4%) (2017~2020) when compared with other manufacturing industries. In particular, it is worth noting that more than 31.8% of nursing accidents in the shipbuilding industry occurred among workers whose job experience is no more than 6 months. Most of new workers are seen to have hard time due to several factors such as lack of work information, inexperience, and unfamiliarity with the working environments. This indicates that it is essential to incorporate more effective training method that could help new workers become familiar with technical skills as well as working environments in a short period of time. Currently, education/training at the domestic shipyard is biased toward technical skills such as welding, painting, machine installation, and electrical installation. Contrary, even more important training required to get new workers used to the working environment has remained at a superficial level such as explaining ship building processes using 2D drawings. This may be the reason why it is inevitable to repeat similar training at OJT (On-the-Job Training) even at the leading domestic companies. Domestic shipbuilding industries have been attracting a lot of new workers thanks to recent economic recovery, which is very likely to increase the occurrence of disasters. In this paper, the introduction of training using XR technology was proposed, and as a specific example, the process of pulling cables using winches on ships was implemented as XR-based training content by using Unity. Using the developed content, it demonstrated that new workers can experience the actual work process in advance through simulation in a virtual space, thereby becoming more effective training content that can help new workers become familiar with the work environment.

• 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.