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복합 전산 공력음향학(CAA) 방법을 이용한 시간영역 풍력터빈 저주파수 소음 예측과 분석
이광세,정철웅,김형택,주원호,Lee, Gwang-Se,Cheong, Cheolung,Kim, Hyung-Taek,Joo, Won-Ho 한국음향학회 2013 韓國音響學會誌 Vol.32 No.5
Using Lowson's acoustic analogy, low frequency noise of a wind turbine (WT) is predicted in time domain and the noise sources contributing to the low frequency noise is analyzed. To compute averaged pressure distribution on blades of the WT as noise source, XFOIL is utilized. The blade source domain is divided into several segments along the span direction to compute force exerted on air surrounding the blade segments, which is used as input for noise prediction. The noise sources are decomposed into three terms of force fluctuation, acceleration and velocity terms and are analyzed to investigate each spectral contribution. Finally, predicted spectra are compared with measured low frequency noise spectrum of a wind turbine in operation. It is found that the force fluctuation component contributes strongly in low frequency range with increasing wind speed. Lowson의 음향상사식을 이용하여 시간영역에서 풍력터빈의 저주파수 소음을 예측 하였고, 관련 소음원들의 기여도를 분석하였다. 소음원으로서 날개-깃 상 평균 압력 분포를 구하기 위하여 XFOIL를 이용하였다. 이 때, 소음 예측 시 입력 값 인 유한 요소 상의 힘을 계산하기 위해 날개-깃을 여러 개의 요소로 분할하였다. 소음원을 힘 섭동항, 가속도항, 속도항으로 분리하여 주파수 기여도를 분석하였다. 끝으로, 예측 스펙트럼을 운용 중 인 풍력터빈에 대하여 측정한 저주파수 소음과 비교하였고, 그 결과 풍속 증가에 따라 힘 섭동 성분이 저주파수에서 크게 기여하는 것을 확인하였다.
점 압력 스펙트럼에 대한 준-이론 모델을 사용한 효율적이고 정확한 평판 뒷전 소음의 예측
이광세(Gwang-Se Lee),정철웅(Cheolung Cheong) 한국소음진동공학회 2012 한국소음진동공학회 학술대회논문집 Vol.2012 No.4
In order to predict trailing edge noise from a flat plate more effectively and accurately, the prediction algorithm based on semi-analytic model for point pressure spectrum is proposed. The semi-analytic model consists of empirical models for point pressure spectra and theoretical model to determine the boundary layer characteristics needed for the empirical models. The proposed methods are applied to predict the trailing edge noise of the flat plate located in the mean flow of speed 38 m/s, for which the measured data are available. In present study, six empirical models for point pressure spectra are utilized for the predictions of trailing edge noise and their prediction results are compared to the measured data. Through the analysis of these comparisons, it is revealed that the present method based on non-frozen formula using Efimtsov model and Smol’yakov-Tkachenko model can provide more accurate and efficient predictions of trailing edge noise.
점 압력 스펙트럼에 대한 준-이론 모델을 사용한 효율적이고 정확한 평판 뒷전 소음의 예측
이광세(Lee, Gwang-Se),정철웅(Cheong, Cheol-Ung) 한국소음진동공학회 2012 한국소음진동공학회 논문집 Vol.22 No.6
In order to predict trailing edge noise from a flat plate more effectively and accurately, the prediction algorithm based on semi-analytic model for point pressure spectrum is proposed. The semi-analytic model consists of empirical models for point pressure spectra and theoretical model to determine the boundary layer characteristics needed for the empirical models. The proposed methods are applied to predict the trailing edge noise of the flat plate located in the mean flow of speed 38 m/s, for which the measured data are available. In present study, six empirical models for point pressure spectra are utilized for the predictions of trailing edge noise and their prediction results are compared to the measured data. Through the analysis of these comparisons, it is revealed that the present method based on non-frozen formula using Efimtsov model and Smol'yakov-Tkachenko model can provide more accurate and efficient predictions of trailing edge noise.
풍력발전기 상태 감시를 위한 SaaS 클라우드 인프라 내 데이터 처리 알고리즘 개선 연구
이광세(Gwang-Se Lee),최정철(Jungchul Choi),강민상(Minsang Kang),박사일(Sail Park),이진재(JinJae Lee) 한국신재생에너지학회 2020 신재생에너지 Vol.16 No.1
In this study, an SW for the analysis of the wind-turbine vibration characteristics was developed as an application of SaaS cloud infrastructure. A measurement system for power-performance, mechanical load, and gearbox vibration as type-test class was installed at a target MW-class wind turbine, and structural meta and raw data were then acquired into the cloud. Data processing algorithms were developed to provide cloud data to the SW. To operate the SW continuously, raw data was downloaded consistently based on the algorithms. During the SW test, an intermittent long time-delay occurred due to the communication load associated with frequent access to the cloud. To solve this, a compression service for the target raw data was developed in the cloud and more stable data processing was confirmed. Using the compression service, stable big data processing of wind turbines, including gearbox vibration analysis, is expected.