Journal of Korean Society for Atmospheric Environment (한국대기환경학회지)

Korean Society for Atmospheric Environment (한국대기환경학회)
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Wind Data Simulation Using Digital Generation of Non-Gaussian Turbulence Multiple Time Series with Specified Sample Cross Correlations

임의의 표본상호상관함수와 비정규확률분포를 갖는 다중 난류시계열의 디지털 합성방법을 이용한 풍속데이터 시뮬레이션

  • Seong, Seung-Hak (Research Institute of Mechanical Technology, Pusan National University) ;
  • Kim, Wook (Department of Mechanical Engineering, College of Engineering, Pusan National University) ;
  • Kim, Kyung-Chun (Department of Mechanical Engineering, College of Engineering, Pusan National University) ;
  • Boo, Jung-Sook (Department of Mechanical Engineering, College of Engineering, Pusan National University)
  • 성승학 (부산대학교 기계기술연구소) ;
  • 김욱 (부산대학교 공과대학 기계공학과) ;
  • 김경천 (부산대학교 공과대학 기계공학과) ;
  • 부정숙 (부산대학교 공과대학 기계공학과)
  • Published : 2003.10.01
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Abstract

A method of synthetic time series generation was developed and applied to the simulation of homogeneous turbulence in a periodic 3 - D box and the hourly wind data simulation. The method can simulate almost exact sample auto and cross correlations of multiple time series and control non-Gaussian distribution. Using the turbulence simulation, influence of correlations, non-Gaussian distribution, and one-direction anisotropy on homogeneous structure were studied by investigating the spatial distribution of turbulence kinetic energy and enstrophy. An hourly wind data of Typhoon Robin was used to illustrate a capability of the method to simulate sample cross correlations of multiple time series. The simulated typhoon data shows a similar shape of fluctuations and almost exactly the same sample auto and cross correlations of the Robin.

Keywords

References

  1. Structural Safety v.8 Approximation and Generation of Gaussian and Non-Gaussian Stationary processes Ammon,D. https://doi.org/10.1016/0167-4730(90)90037-P
  2. Time Series: Theory and Methods(2nd Ed.) Brockwell,P.J.;R.A.Davis
  3. Boundary-Layer Meteorology v.98 Large-Eddy Simulation of Neutral Turbulent Flow Over Rough Sinusoidal Ridges Brown,A.R.;J.M.Hobson;N.Wood https://doi.org/10.1023/A:1018703209408
  4. J. Applied Meteorology v.34 Estimation of extreme wind speeds wirh very long return periods Dukes;Palutikov https://doi.org/10.1175/1520-0450(1995)034<1950:EOEWSW>2.0.CO;2
  5. Statistical Theory and Modeling for Turblulent Flows Durbin,P.A.;B.A.Pettersson
  6. Computational Methods for Turbulent Transsonic and Viscous Flows Ferziger,J.
  7. Adv. Appl. Prob. v.12 First-order autoregressive gamma sequences and point processes Gaver,D.P.;P.A.W.Lewis https://doi.org/10.2307/1426429
  8. Physics of Fluids v.11 Diffusion by a random velocity field Kraichnan,R. https://doi.org/10.1063/1.1691777
  9. J. of Computational Physics v.140 Generation of Turbulent Inflow Data for Spatially-Developing Boundary Layer Simulations Lund,T.S.;X.Wu;K.D.Squires https://doi.org/10.1006/jcph.1998.5882
  10. J. Wind Eng. Ind. Aerodyn. v.73 Digital generation of surface pressure fluctuations with spiky features Seong,S.H.;J.A.Peterka https://doi.org/10.1016/S0167-6105(97)00283-3
  11. Int. Symposium on Wind and Structures Estimation of Extreme Wind Speeds Based on Synthetic Long-Term Hourly Data Seong,S.H.;J.A.Peterka
  12. J. Wind Eng. Ind. Aerodyn v.89 Experiments on Fourier Phases for Synthesis of Non-Gaussian Spikes In Turbulence Time Series Seong,S.H.;J.A.Peterka https://doi.org/10.1016/S0167-6105(00)00073-8
  13. Applied Mechanics Review v.44 no.4 Simulation of stochastic process by spectral representation Shinozuka,M. https://doi.org/10.1115/1.3119501
  14. A First Course in Turbulence Tennekes,H.;J.L.Lumley