• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2011년 춘계학술대회논문집
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• Wind and Structures
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• 제5권2_3_4호
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• pp.227-244
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• 2002

Recently, the prediction of wind environment around a building using Computational Fluid Dynamics (CFD) technique comes to be carried out at the practical design stage. However, there have been very few studies which examined the accuracy of CFD prediction of flow around a high-rise building including the velocity distribution at pedestrian level. The working group for CFD prediction of wind environment around building, which consists of researchers from several universities and private companies, was organized in the Architectural Institute of Japan (AIJ) considering such a background. At the first stage of the project, the working group planned to carry out the cross comparison of CFD results of flow around a high rise building by various numerical methods, in order to clarify the major factors which affect prediction accuracy. This paper presents the results of this comparison.

• 신재생에너지
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• 제8권4호
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• pp.3-12
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• 2012

Large offshore wind farms have actively been developed in order to meet the needs for wind energy since the land-based wind farms have almost been fully developed especially in Europe. The key problem for the construction of offshore wind farms may be on the high cost of energy compared to land-based ones. NREL (National Renewable Energy Laboratory) has developed a spreadsheet-based tool to estimate the cost of wind-generated electricity from both land-based and offshore wind turbines. Component formulas for various kinds and scales of wind turbines were made using available field data. In this paper, this NREL estimation model is introduced and applied to the offshore wind turbines now under designing or in production in Korea, and the result is discussed.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• 한국전산유체공학회지
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• 제17권1호
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• pp.54-60
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• 2012

Transition prediction results are validated with experimental data obtained from a transonic wind tunnel for the KU109C airfoil. A Reynolds-Averaged Navier-Stokes code is simultaneously coupled with the transition transport model of Langtry and Menter and applied to the numerical prediction of aerodynamic performance of the KU109C airfoil. Drag coefficients from the experiment are better correlated to the numerical prediction results using a transition transport model rather than the fully turbulent simulation results. Maximum lift coefficient and drag divergence at the zero-lift condition with Mach number are investigated. Through the present validation procedure, the accuracy and usefulness of both the experiment and the numerical prediction are assessed.

• 전기학회논문지
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• 제64권12호
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• pp.1748-1755
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• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.

• 대한조선학회논문집
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• 제50권4호
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• pp.206-216
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• 2013

The present study considers the prediction of wind-induced heel of cruise ship and its stabilization. Wind load in ocean exerts on the surface of superstructure of cruise ship, which causes the heel moment on the ship. The calculation of wind load starts from choosing wind speed profile, so that the logarithmic wind profile model is applied in this study. Heel moment by wind load is calculated by adopting approximate formulation and applied to the ship motion analysis in time domain. Motion stabilizers, such as stabilizing fin and U-tube tank, are considered to reduce the heel effect as well as excessive roll motion. From this study, it is expected that the present method can be applied to the prediction and stabilization of the heel motion of cruise ships.

• 조명전기설비학회논문지
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• 제29권7호
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• pp.80-86
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• 2015

As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

• Wind and Structures
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• 제11권1호
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• pp.35-50
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• 2008

Wind and temperature have been shown to be the critical sources causing changes in the modal properties of large-scale bridges. While the individual effects of wind and temperature on modal variability have been widely studied, the investigation about the effects of multiple environmental factors on structural modal properties was scarcely reported. This paper addresses the modeling of the simultaneous effects of wind and temperature on the modal frequencies of an instrumented cable-stayed bridge. Making use of the long-term monitoring data from anemometers, temperature sensors and accelerometers, a neural network model is formulated to correlate the modal frequency of each vibration mode with wind speed and temperature simultaneously. Research efforts have been made on enhancing the prediction capability of the neural network model through optimal selection of the number of hidden nodes and an analysis of relative strength of effect (RSE) for input reconstruction. The generalization performance of the formulated model is verified with a set of new testing data that have not been used in formulating the model. It is shown that using the significant components of wind speeds and temperatures rather than the whole measurement components as input to neural network can enhance the prediction capability. For the fundamental mode of the bridge investigated, wind and temperature together apply an overall negative action on the modal frequency, and the change in wind condition contributes less to the modal variability than the change in temperature.

• 한국농림기상학회지
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• 제23권4호
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• pp.222-234
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• 2021

바람에 의해 해외지역에서 국내로 유입되는 비래해충들은 주요 작물에 상당한 피해를 초래할 수 있다. 바람에 의한 비래해충의 이동 경로를 추정하기 위해 기상 모형들이 사용되는데, 본 연구에서는 비래해충이 도달할 수 있는 지역을 예측할 때 입력설정이 미치는 영향을 분석하였다. 벼멸구가 중국에서 국내로 유입된다는 가정하에 입자의 바람이동 경로를 추적하기 위해 개발된 HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) 모형을 사용하여 바람의 이동경로를 예측하였다. 중국, 한국 및 일본이 포함된 중규모 수치기상모형 자료를 사용하여 순간 및 평균 풍속자료가 포함된 기상입력자료를 생성하였다. 또한, 이동 경로 계산을 위해 계산 시간 간격을 1, 30, 60분으로 설정하였다. 중국에서 벼멸구가 관측된 지점에서 2019년과 2021년 6월 상순 기간 동안 바람의 이동 경로를 계산한 결과, 순간 풍속과 평균 풍속자료를 사용함에 따라 비래해충 도달지점에 큰 차이가 나타났다. 계산 시간에 따른 이동 경로 결과값들의 공간적 분포는 상대적으로 유사도가 높았으며, 순간풍속을 사용한 경우 벼멸구 관측지역과 비교적 유사한 경향이 나타났다. 이러한 결과는 바람 경로를 추적하여 비래해충 도착지점을 추정할 때 사용되는 입력자료의 특성을 파악하고 이들로부터 발생하는 불확도에 대한 고려가 필요함을 시사한다.