• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1059-1069
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• 2023

Dam-break flow occurs when an elevated dam suddenly collapses, resulting in the catastrophic release of rapid and uncontrolled impounded water. This study compares laminar and turbulent closure models for simulating three-dimensional dam-break flows using OpenFOAM. The Reynolds-Averaged Navier-Stokes (RANS) model, specifically the k-ε model, is employed to capture turbulent dissipation. Two scenarios are evaluated based on a laboratory experiment and a modified multi-layered block obstacle scenario. Both models effectively represent dam-break flows, with the turbulent closure model reducing oscillations. However, excessive dissipation in turbulent models can underestimate water surface profiles. Improving numerical schemes and grid resolution enhances flow recreation, particularly near structures and during turbulence. Model stability is more significantly influenced by numerical schemes and grid refinement than the use of turbulence closure. The k-ε model's reliance on time-averaging processes poses challenges in representing dam-break profiles with pronounced discontinuities and unsteadiness. While simulating turbulence models requires extensive computational efforts, the performance improvement compared to laminar models is marginal. To achieve better representation, more advanced turbulence models like Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) are recommended, necessitating small spatial and time scales. This research provides insights into the applicability of different modeling approaches for simulating dam-break flows, emphasizing the importance of accurate representation near structures and during turbulence.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.481-491
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• 2014

The power production using renewable energy is more important because of a limited amount of fossil fuel and the problem of global warming. A concentrative photovoltaic system comes into the spotlight with high energy production, since the rate of power production using solar energy is proliferated. These systems, however, need to sophisticated tracking methods to give the high power production. In this paper, we propose a hierarchical tracking system using modular Bayesian networks and a naive Bayes classifier. The Bayesian networks can respond flexibly in uncertain situations and can be designed by domain knowledge even when the data are not enough. Bayesian network modules infer the weather states which are classified into nine classes. Then, naive Bayes classifier selects the most effective method considering inferred weather states and the system makes a decision using the rules. We collected real weather data for the experiments and the average accuracy of the proposed method is 93.9%. In addition, comparing the photovoltaic efficiency with the pinhole camera system results in improved performance of about 16.58%.

• Journal of Energy Engineering
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• v.25 no.3
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• pp.18-26
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• 2016

Mineral oil is the most widely used for electrical transformer, though some factors should be considered such as their environmentally harmfulness when it spill and low flash point. To cover these disadvantages, vegetable oil has developed because of its high biodegradability and thermal stability. However, it is necessary that many studies should conduct to reveal the detailed impacts of long-term operation as transformer oil. In this paper, we applied the accelerated aging test which simulate the real transformer circumstances using insulation paper, coil, steel at $150^{\circ}C$, which is higher than normal operation, for 2 weeks. To figure out the oxidation characteristics between mineral oil and vegetable oil test major properties and components such as total acid number, dielectric breakdown and dissolved gas components during that period. As a result of these tests, we found that vegetable oil has higher electric insulation ability than mineral oil though poor total acid number by hydrophile property. Vegetable oil also kept its thermal stability under the given circumstances.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.99-111
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• 2009

In wireless sensor networks, various schemes have been proposed to store and process sensed data efficiently. A Data-Centric Storage(DCS) scheme assigns distributed data regions to sensors and stores sensed data to the sensor which is responsible for the data region overlapping the data. The DCS schemes have been proposed to reduce the communication cost for transmitting data and process exact queries and range queries efficiently. Recently, KDDCS that readjusts the distributed data regions dynamically to sensors based on K-D tree was proposed to overcome the storage hot-spots. However, the existing DCS schemes including KDDCS suffer from Query Hot-Spots that are formed if the query regions are not uniformly distributed. As a result, it causes reducing the life time of the sensor network. In this paper, we propose a new DCS scheme, called TPDCS(Time-Parameterized DCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed. data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the life time of sensor networks. It is shown through various experiments that our scheme outperform the existing schemes.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• KIEAE Journal
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• v.15 no.5
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• pp.113-118
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• 2015

Purpose: In Korea, buildings make up 20.5% (2012) of the gross national energy consumption, so they are a major target for greenhouse gas reduction. In particular, energy consumption in multihousing represents approximately 32.6% of the entire building sector. With improving energy performance being the focus, efforts are continuously being made to reinforce standards and systems in greenhouse gas reduction. This study investigated the current status of multihousing in Korea in terms of energy performance as described in the performance evaluation reports submitted (to an institution that specializes in reviewing the performance evaluation of green homes) based on the construction criteria and performance codes for green homes and examined if the evaluation criteria using related methodologies were appropriate. The results will provide helpful information for reviewing the future directions of operations and amendments to the systems. Method: The overall characteristics of the system were examined using the evaluation methodologies (and current state of revisions) of the performance codes for green homes and comparing them with similar systems. Also, the current state of application and energy performance (conducted according to the evaluation methodologies) were compared by the evaluation institution for multihousing neighborhoods that were assessed for five years from 2010 to 2014. Result: It has been confirmed that the performance codes for green homes are different from other similar systems in evaluating performances of multihousing in that they allow both quantitative and qualitative methods of evaluation, and they consider both energy and sustainability simultaneously in the evaluation. Furthermore, regarding the adoption rate of the forms for the two evaluation methods (Form 1 - quantitative and Form 2 - qualitative), the rate preferring Form 2 increased gradually in time to reach 55.3% in 2014. In analyzing the rate of overall energy reduction (submitted in Form 1) and the coefficient of thermal transmission for each part (submitted in Form 2), it was observed that the deviation between the performance submitted and the criteria decreased in line with the level of reinforcement.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1255-1263
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• 2021

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.1-6
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• 2021

Mercury, once released, is not destroyed but accumulates and circulates in the natural environment, causing serious harm to ecosystems and human health. In the United States, sulfur-impregnated activated carbon is being considered for the removal of vapor mercury from the flue gas of coal-fired power plants, which accounts for about 32 % of the anthropogenic emissions of mercury. In this study, a high-efficiency porous mercury adsorption material was developed to reduce the mercury vapor in the exhaust gas of coal combustion facilities, and the mercury adsorption characteristics of the material were investigated. As a result of the investigation of the vapor mercury adsorption capacity at 30℃, the silica nanotube MCM-41 was only about 35 % compared to the activated carbon Darco FGD commercially used for mercury adsorption, but it increased to 133 % when impregnated with 1.5 % sulfur. In addition, the furnace fly ash recovered from the waste copper regeneration process showed an efficiency of 523 %. Furthermore, the adsorption capacity was investigated at temperatures of 30 ℃, 80 ℃, and 120 ℃, and the best adsorption performance was found to be 80 ℃. MCM-41 is a silica nanotube that can be reused many times due to its rigid structure and has additional advantages, including no possibility of fire due to the formation of hot spots, which is a concern when using activated carbon.

• Informatization Policy
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• v.30 no.4
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• pp.40-61
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• 2023

The adoption of smart factories and smart manufacturing as strategies to enhance competitiveness and stimulate growth in the manufacturing sector is vital for a country's future competitiveness and industrial transformation. The government has consistently pursued smart manufacturing innovation policies starting with the Manufacturing Innovation 3.0 strategy in the Ministry of Industry. This study aims to identify policy areas for smart factories and smart manufacturing based on technical standards. Analyzing policy areas at the current stage where the establishment and support of domestic standards aligning with international technical standards are required is crucial. By prioritizing smart manufacturing process areas within the industry, policymakers can make well-informed decisions to advance smart manufacturing without blindly following international standardization in already well-established areas. To achieve this, the study utilizes a hierarchical analysis method including expert interviews and importance-performance analysis for the five major process areas. The findings underscore the importance of proactive participation in standardization for emerging technologies, such as data and security, instead of solely focusing on areas with extensive international standardization. Additionally, policymakers need to consider carbon emissions, energy costs, and global environmental challenges to address international trends in export and digital trade effectively.

• KSBB Journal
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• v.14 no.3
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• pp.328-336
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• 1999

A green microalga, Chlorella vulgaris UTX 259, was cultivated in a bench-scale raceway pond. During the culture, 15%(v/v) $CO_2$ was supplied and industrial wastewater discharged from a steel-making plant was used as a culture medium. In a small scale culture bottle, the microalga grew up to 1.8 g $dm^{-3}$ of cell concentration and ammonia was completely removed from the wastewater with an yield coefficient of 25.7 g dry cell weight $g^{-1}\;NH_3-N$. During the bottle-culture, microalga was dominant over heterotrophic microorganisms in the culture medium. Therefore, the amount of carbon dioxide fixation could be estimated from the change of dry cell weight. In a semi-continuous operation of raceway pond with intermittent lighting (12 h light and 12 h dark), increase of dilution rate resulted in increase of the ammonia removal rate as well as the $CO_2$ fixation rate but the ammonia removal efficiency decreased. Ammonia was not completely removed from the medium (wastewater) of raceway pond which was operated in a batch mode under a light intensity up to 20 klux. The incomplete removal of ammonia was believed due to insufficient light supply. A mathematical model, capable of predicting experimental data, was developed in order to simulate the performance of the raceway pond under the light intensity of sun during a bright daytime. Simulation results showed that the rates of $CO_2$ fixation and ammonia removal could be enhanced by increasing light intensity. According to the simulation, 80 mg $dm^{-3}$ of ammonia in the medium could be completely removed if the light intensity was over 60 klux with a continuous lighting. Under the optimal operating condition determined by the simulation, the rates of carbon dioxide fixation and ammonia removal in the outdoor operation of raceway pond were estimated as high as $24.7 g m^{-2} day^{-1}$ and $0.52 g NH_3-N m^{-2} day^{-1}$, respectively.