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상용 CFD 프로그램을 이용한 복잡지형에서의 풍속 예측

Wind Speed Prediction in Complex Terrain Using a Commercial CFD Code

  • 우재균 (강원대학교 대학원 기계메카트로닉스공학과) ;
  • 김현기 (강원대학교 대학원 기계메카트로닉스공학과) ;
  • 백인수 (강원대학교 기계메카트로닉스공학과) ;
  • 유능수 (강원대학교 기계메카트로닉스공학과) ;
  • 남윤수 (강원대학교 기계메카트로닉스공학과)
  • Woo, Jae-Kyoon (Dept. of Mechanical and Mechatronics Engineering, Kangwon National University, Graduate School) ;
  • Kim, Hyeon-Gi (Dept. of Mechanical and Mechatronics Engineering, Kangwon National University, Graduate School) ;
  • Paek, In-Su (Dept. of Mechanical and Mechatronics Engineering, Kangwon National University) ;
  • Yoo, Neung-Soo (Dept. of Mechanical and Mechatronics Engineering, Kangwon National University) ;
  • Nam, Yoon-Su (Dept. of Mechanical and Mechatronics Engineering, Kangwon National University)
  • 투고 : 2011.07.14
  • 심사 : 2011.09.21
  • 발행 : 2011.12.30

초록

Investigations on modeling methods of a CFD wind resource prediction program, WindSim for a ccurate predictions of wind speeds were performed with the field measurements. Meteorological Masts having heights of 40m and 50m were installed at two different sites in complex terrain. The wind speeds and direction were monitored from sensors installed on the masts and recorded for one year. Modeling parameters of WindSim input variables for accurate predictions of wind speeds were investigated by performing cross predictions of wind speeds at the masts using the measured data. Four parameters that most affect the wind speed prediction in WindSim including the size of a topographical map, cell sizes in x and y direction, height distribution factors, and the roughness lengths were studied to find out more suitable input parameters for better wind speed predictions. The parameters were then applied to WindSim to predict the wind speed of another location in complex terrain in Korea for validation. The predicted annual wind speeds were compared with the averaged measured data for one year from meteorological masts installed for this study, and the errors were within 6.9%. The results of the proposed practical study are believed to be very useful to give guidelines to wind engineers for more accurate prediction results and time-saving in predicting wind speed of complex terrain that will be used to predict annual energy production of a virtual wind farm in complex terrain.

키워드

참고문헌

  1. K.H. Yoon, A Study on the Changes of the US Energy Policy and the Expansion of the New and Renewable Energy, J. of Korean Reg. Dev. 8 (2008) 151-179.
  2. J. S. Hwang, H. J. Lee, S.H. Park, Green IT Policies for Low Carbon, Green Growth, J. of Korean Assoc. for Inform. Soc.14 (2008) 3-28.
  3. J.B. Kil, B.K. Jung, Green Growth and Environment-Economy Integration: Between Modification and Transition, J. of Korean Inst. of Gov. Studies,15 (2009) 45-71.
  4. J.M.L.M. Palma, F.A. Castro, L.F. Ribeiro, A.H. Rodrigues, A.P. Pinto, Linear and nonlinear models in wind resource assessment and windturbine micro-sitingin complex terrain, J. of Wind Eng. and Ind. Aerodyn. 96 (2008) 2308-2326. https://doi.org/10.1016/j.jweia.2008.03.012
  5. A. Llombart, A. talayero, A. Mallet, and E. Telmo, Performance Analysis of Wind Resource Assessment Programs in Complex Terrain, Int'l. Conf. on Renew. Energies and Power Qual., 2006.
  6. Risoe Laboratory, available online at http://www.wasp.dk/,last last seen in March 2010.
  7. P. Moreno, A. R. Gravdahl, and M. Romero, Wind Flow over Complex Terrain: application of Linear and CFD Models, European Wind Energy Conf. and Exhib., Madrid, 2003.
  8. T. Wallbank, WidSim Validation Study, CFD validation in Complex terrain, 2008. Available online at http://web.windsim.com/library/papers--presentations.aspx, lastaccessedinMarch2010.
  9. G. Waston, N. Doublas, S. Hall, Comparison of Wind Flow Models in Complex Terrain, World Renew. Energy Congr., 2005.
  10. K. Yoon, I. Paek, N. S. Yoo, Wind Speed Prediction using WAsP for Complex Terrain, Proc. of the 2008 autumn Conf. of Korea Wind Energy Assoc., 2008.
  11. S. -W. Kim, H. -G. Kim, Sensitivity Analysis of Wind Resource Micrositing at the Antarctic King Sejong Station, J. of the Korean Solar Energy Soc. 27 (2007) 1-9.
  12. R. Cattin, B. Schaffner, S. Kunz, Validation of CFD Wind Resource Modeling in Highly Complex Terrain, European Wind Energy Conf., Athens, 2006.
  13. J. Maza, G. Nicoletti, "CFD-RANS applications in complex terrain analysis. Numerical vs experimental results. A case study: Cozzovallefondi wind farm in Sicily" European Wind Energy Conf., Athens, 2006
  14. Y. Hwang, I. Paek, K. Yoon, W. Lee, N. Yoo and Y. Nam, "Application of wind data from automated weather stations to wind resources estimation in Korea," Journal of Mechanical Science and Technology, 2010.
  15. N.G. Mortensen and E.L. Peterson, "Influence of topographical input data on the accuracy of wind flow modeling in complex terrain," RisO National Laboratory, Roskilde, Denmark
  16. A.J. Bowen and N.G. Mortensen, "WAsP prediction errors due to site orography," RisO National Laboratory, Roskilde, Denmark, 2004.
  17. Available online at www.cham.co.uk ,last seen in March 2010.
  18. D. Fallo, "wind energy resource evaluation in a site of central Italy ny CFD simulations", Ph. D. Dissertation, Univ. of Cagliari, DiMeCa, 2007.
  19. WindSim 4.8.1 Getting started, WindSim AS, Norway, 2008.
  20. Developing Wind Power Projec
  21. G.N. Kor, J. Ch. Huh, Introduction of Wind Energy Engineering, Munundang, Korea. 2008.
  22. WindPRO Manual

피인용 문헌

  1. Prediction of Annual Energy Production of Wind Farms in Complex Terrain using MERRA Reanalysis Data vol.34, pp.2, 2014, https://doi.org/10.7836/kses.2014.34.2.082