• Journal of Wind Energy
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• v.14 no.4
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• pp.43-49
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• 2023

We have studied biofouling in Korea's offshore wind farms by using image analysis through monitoring and surface energy analysis. To observe the biofouling characteristics, samples were fabricated using Micron extra 2 and PropOne, which have a self-polishing property, and Hempathane HS 55610, which is used in substructure coatings. The manufactured samples were installed at the bottom of a ladder in a substructure, and monitored for 10 months. The most biofouling occurred in the sample without the self-polishing property, and algae, barnacles and corallinales were observed. The surface energy analysis used the Owens-Wendt-Rabel and Kaelble (OWRK) model, which uses the contact angles of two standard fluids. As a result of calculating the surface energy using contact angle measurement, the sample without the self-polishing property showed the highest value. This result was consistent with the biofouling incidence observed through monitoring.

• Applied Chemistry for Engineering
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• v.30 no.2
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• pp.178-185
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• 2019

Three spherical quaternary composites composed of metal/metal oxide/high explosive/oxidizer were prepared by a crystallization/agglomeration process. From the characteristics of composites by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), the shortening of the decomposition zone of high explosives in the quaternary composite was observed, which may be attributed to the autocatalytic reaction caused by $ClO_2$ or HCl which are ammonium perchlorate (AP) degradation products. The activation energy analysis showed that the activation energy abruptly decreases at the end of the decomposition zone of high explosives, and it was considered to be caused by $HNO_2$ which is common in decomposition products of high explosives. The activation energy predicted from complex pyrolysis results by the distributed activation energy model (DAEM) showed much better in accuracy than those by model-fitting methods such as Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa models.

• Corrosion Science and Technology
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• v.18 no.2
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2168-2177
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• 2018

The unavoidable forecast error of wind power is one of the biggest obstacles for wind farms to participate in day-ahead electricity market. To mitigate the deviation from forecast, installation of energy storage system (ESS) is considered. An accurate model of wind power forecast error is fundamental for ESS sizing. However, previous study shows that the error distribution has variable kurtosis and fat tails, and insufficient measurement data of wind farms would add to the difficulty of modeling. This paper presents a comprehensive way that makes the use of mixed skewness model (MSM) and copula theory to give a better approximation for the distribution of forecast error, and it remains valid even if the dataset is not so well documented. The model is then used to optimize the ESS power and capacity aiming to pay the minimal extra cost. Results show the effectiveness of the new model for finding the optimal size of ESS and increasing the economic benefit.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2080-2094
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• 2004

A model predictive controller is designed to control thermal power in a nuclear reactor. The basic concept of the model predictive control is to solve an optimization problem for finite future time steps at current time, to implement only the first optimal control input among the solved control inputs, and to repeat the procedure at each subsequent instant. A controller design model used for designing the model predictive controller is estimated every time step by applying a recursive parameter estimation algorithm. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), was used to verify the proposed controller for a nuclear reactor. It was known that the nuclear power controlled by the proposed controller well tracks the desired power level and the desired axial power distribution.

• Nuclear Engineering and Technology
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• v.44 no.6
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• pp.639-652
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• 2012

A three-dimensional discrete fracture network model was developed in order to simulate the hydraulic characteristics of a granitic rock mass at Korea Atomic Energy Research Institute (KAERI) Underground Research Tunnel (KURT). The model used a three-dimensional discrete fracture network (DFN), assuming a correlation between the length and aperture of the fractures, and a trapezoid flow path in the fractures. These assumptions that previous studies have not considered could make the developed model more practical and reasonable. The geologic and hydraulic data of the fractures were obtained in the rock mass at the KURT. Then, these data were applied to the developed fracture discrete network model. The model was applied in estimating the representative elementary volume (REV), the equivalent hydraulic conductivity tensors, and the amount of groundwater inflow into the tunnel. The developed discrete fracture network model can determine the REV size for the rock mass with respect to the hydraulic behavior and estimate the groundwater flow into the tunnel at the KURT. Therefore, the assumptions that the fracture length is correlated to the fracture aperture and the flow in a fracture occurs in a trapezoid shape appear to be effective in the DFN analysis used to estimate the hydraulic behavior of the fractured rock mass.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.760-768
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• 2017

The transition temperature shift (TTS) of the reactor pressure vessel materials is an important factor that determines the lifetime of a nuclear power plant. The prediction of the TTS at the end of a plant's lifespan is calculated based on the equation of Regulatory Guide 1.99 revision 2 (RG1.99/2) from the US. The fluence factor in the equation was expressed as a power function, and the exponent value was determined by the early surveillance data in the US. Recently, an advanced approach to estimate the TTS was proposed in various countries for nuclear power plants, and Korea is considering the development of a new TTS model. In this study, the TTS trend of the Korean surveillance test results was analyzed using a nonlinear regression model and a mixed-effect model based on the power function. The nonlinear regression model yielded a similar exponent as the power function in the fluence compared with RG1.99/2. The mixed-effect model had a higher value of the exponent and showed superior goodness of fit compared with the nonlinear regression model. Compared with RG1.99/2 and RG1.99/3, the mixed-effect model provided a more accurate prediction of the TTS.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.237.1-237.1
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• 2010

Recent developments such as concern over global warming, depletion of fossil fuels and increase in energy demands by the increasing world population has eventually lead to mass production of electricity using renewable sources. Ocean contains energy in form of thermal energy and mechanical energy: thermal energy from solar radiation and mechanical energy from the waves and tides. The current paper looks at generating power using waves. The primary objective of the present study is to maximize the primary energy conversion (first stage conversion) of the base model by making some design changes. The model entire consisted of a numerical wave tank and the turbine section. The turbine section had three components; front guide nozzle, augmentation channel and the rear chamber. The augmentation channel further consisted of a front nozzle, rear nozzle and an internal fluid region representing the turbine housing. Different front guide nozzle configuration and rear chamber design were studied. As mentioned, a numerical wave tank was utilized to generate waves of desired properties and later the turbine section was integrated. The waves in the numerical wave tank were generated by a piston type wave maker which was located at the wave tank inlet. The inlet which was modeled as a plate wall which moved sinusoidally with the general function, $x=asin{\omega}t$. In addition to primary energy conversion, observation of flow characteristics, pressure and the velocity in the augmentation channel, rear chamber as well as the front guide nozzle are presented in the paper. The analysis was performed using the commercial code of the ANSYS-CFX. The base model recorded water power of 29.9 W. After making the changes, the best model obtained water power of 37.1 W which represents an increase of approximately 24% in water power and primary energy conversion.

• Journal of Energy Engineering
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• v.24 no.2
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• pp.59-71
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• 2015

This study attempts to examine the economic impacts of electricity industry in Korea and Japan using an inter-industry analysis. Specifically, the study analyzes and compares electricity industry between Japan and Korea through production-inducing effect and value added inducing effect of electricity industry based on demand-driven model. Moreover, this study deals with supply shortage effect and sectoral price effect by using supply-driven model and Leontief price model, respectively. This study analyses the electricity industry through exogenous approach. The results show that electricity industry induces prodution-inducing effect of 0.5946 won in other industries in Korea and 0.5446 yen in other industries in Japan. Value-added-inducing effects are 0.1716 won in other in other industries in Korea and 0.2929 yen in other industries in Japan. Supply shortage effects of electricity industry are 1.5932 won in other industries in Korea and 1.2801 yen in other industries in Japan. And sectoral price effects are 0.2113% in Korea and 0.2196% in Japan due to the price increase of 10% of electricity industry.