On von Kármán spectrum from a view of fractal

Ming Li; Jianxing Leng

Waves, Wavelets and Fractals (2015)

  • Volume: 1, Issue: 1
  • ISSN: 2449-5557

Abstract

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Von Kármán originally deduced his spectrum of wind speed fluctuation based on the Stockes-Navier equation. That derivation, however, is insufficient to exhibit the fractal information of time series, such as wind velocity fluctuation. This paper gives a novel derivation of the von Kármán spectrum based on fractional Langevin equation, aiming at establishing the relationship between the conventional von Kármán spectrum and fractal dimension. Thus, the present results imply that a time series that follows the von Kármán spectrum can be taken as a specifically fractional Ornstein-Uhlenbeck process with the fractal dimension 5/3, providing a new view of the famous spectrum of von Kármán’s from the point of view of fractals. More importantly, that also implies a novel relationship between two famous spectra in fluid mechanics, namely, the Kolmogorov’s spectrum and the von Kármán’s. Consequently, the paper may yet be useful in practice, such as ocean engineering and shipbuilding.

How to cite

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Ming Li, and Jianxing Leng. "On von Kármán spectrum from a view of fractal." Waves, Wavelets and Fractals 1.1 (2015): null. <http://eudml.org/doc/276614>.

@article{MingLi2015,
abstract = {Von Kármán originally deduced his spectrum of wind speed fluctuation based on the Stockes-Navier equation. That derivation, however, is insufficient to exhibit the fractal information of time series, such as wind velocity fluctuation. This paper gives a novel derivation of the von Kármán spectrum based on fractional Langevin equation, aiming at establishing the relationship between the conventional von Kármán spectrum and fractal dimension. Thus, the present results imply that a time series that follows the von Kármán spectrum can be taken as a specifically fractional Ornstein-Uhlenbeck process with the fractal dimension 5/3, providing a new view of the famous spectrum of von Kármán’s from the point of view of fractals. More importantly, that also implies a novel relationship between two famous spectra in fluid mechanics, namely, the Kolmogorov’s spectrum and the von Kármán’s. Consequently, the paper may yet be useful in practice, such as ocean engineering and shipbuilding.},
author = {Ming Li, Jianxing Leng},
journal = {Waves, Wavelets and Fractals},
keywords = {The von Kármán spectrum; fluctuation of wind speed; fractional Ornstein-Uhlenbeck process; Kolmogorov’s −5/3 law},
language = {eng},
number = {1},
pages = {null},
title = {On von Kármán spectrum from a view of fractal},
url = {http://eudml.org/doc/276614},
volume = {1},
year = {2015},
}

TY - JOUR
AU - Ming Li
AU - Jianxing Leng
TI - On von Kármán spectrum from a view of fractal
JO - Waves, Wavelets and Fractals
PY - 2015
VL - 1
IS - 1
SP - null
AB - Von Kármán originally deduced his spectrum of wind speed fluctuation based on the Stockes-Navier equation. That derivation, however, is insufficient to exhibit the fractal information of time series, such as wind velocity fluctuation. This paper gives a novel derivation of the von Kármán spectrum based on fractional Langevin equation, aiming at establishing the relationship between the conventional von Kármán spectrum and fractal dimension. Thus, the present results imply that a time series that follows the von Kármán spectrum can be taken as a specifically fractional Ornstein-Uhlenbeck process with the fractal dimension 5/3, providing a new view of the famous spectrum of von Kármán’s from the point of view of fractals. More importantly, that also implies a novel relationship between two famous spectra in fluid mechanics, namely, the Kolmogorov’s spectrum and the von Kármán’s. Consequently, the paper may yet be useful in practice, such as ocean engineering and shipbuilding.
LA - eng
KW - The von Kármán spectrum; fluctuation of wind speed; fractional Ornstein-Uhlenbeck process; Kolmogorov’s −5/3 law
UR - http://eudml.org/doc/276614
ER -

References

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  1. [1] T. von Karman, Progress in the statistical theory of turbulence, Proc. N. A. S., vol. 34, no. 11, pp. 530–539, 1948.  Zbl0032.22601
  2. [2] G. H. Goedecke, V. E. Ostashev, D. K. Wilson, and H. J. Auvermann, Quasi-wavelet model of von Kármán spectrum of turbulent velocity fluctuations, Boundary-Layer Meteorology, vol. 112, no. 1, pp. 33–56, 2004.  
  3. [3] E. E. Morfiadakis, G. L. Glinou, and M. J. Koulouvari, The suitability of the von Karman spectrum for the structure of turbulence in a complex terrain wind farm, Journal ofWind Engineering and Industrial Aerodynamics, vol. 62, no. 2–3, pp. 237–257, 1996.  
  4. [4] M. C. H. Hui, A. Larsen, and H. F. Xiang,Wind turbulence characteristics study at the stonecutters bridge site: part IIwind power spectra, integral length scales and coherences, Journal of Wind Engineering and Industrial Aerodynamics, vol. 97, no. 1, pp. 48– 59, 2009. [WoS][Crossref] 
  5. [5] G. Huang and X. Chen, Wavelets-based estimation of multivariate evolutionary spectra and its application to nonstationary downburst winds, Engineering Structures, vol. 31, no. 4, pp. 976–989, 2009. [Crossref][WoS] 
  6. [6] D. K.Wilson, V. E. Ostashev, and G. H. Goedecke, Quasi-wavelet formulations of turbulence and other random fields with correlated properties, Probabilistic Engineering Mechanics, vol. 24, no. 3, pp. 343–357, 2009. [Crossref][WoS] 
  7. [7] G. Li and Q. Li, Theory of Time-Varying Reliability for Engineering Structures and Its Applications, Science Press, 2001. (In Chinese)  
  8. [8] J. Pang, Z. Lin, and Y. Lu, Discussion on the simulation of atmospheric boundary layer with spires and roughness elements in wind tunnels, Experiments and Measurements in Fluid Mechanics, vol. 18, no. 2, pp. 32–37, 2004. (In Chinese)  
  9. [9] Y.-Q. Xiao, J.-C. Sun, and Q. Li, Turbulence integral scale and fluctuation wind speed spectrumof typhoon: an analysis based on field measurements, Journal of Natural Disasters, vol. 15, no. 5, pp. 45–53, 2006. (In Chinese)  
  10. [10] J. C. Kaimal, J. C. Wyngaard, Y. Yzumi, and O. R. Cote, Spectral characteristics of surface layer turbulence, Quarterly Journal of the Royal Meteorological Society, vol. 98, no. 417, pp. 563–589, 1972. [Crossref][WoS] 
  11. [11] H. Panofsky, D. Larko, R. Lipschutz, and G. Stone, Spectra of velocity components over complex terrain, Quarterly Journal of the Royal Meteorological Society, vol. 108, no. 455, pp. 215–230, 1982. [Crossref] 
  12. [12] Gaoan Xiushu (Japan), Fractal, Seism Press (China), 1989, (Translation in Chinese by B.-M. Shen and Z.-W. Chang)  
  13. [13] K. S. Miller and B. Ross, An Introduction to the Fractional Calculus and Fractional Differential Equations, John Wiley, 1993.  Zbl0789.26002
  14. [14] M. D. Ortigueira, Introduction to fractional linear systems. part i: continuous-time systems, IEE Proc. Vis. Image Sig. Proc., vol. 147, no. 1, pp. 62–70, 2000.  
  15. [15] M. D. Ortigueira, Introduction to fractional linear systems. part ii: discrete-time systems, IEE Proc. Vis. Image Sig. Proc., vol. 147, no. 1, pp. 71–78, 2000.  
  16. [16] I. Podlubny, Fractional-order systems and PIlDμ-controllers, IEEE Trans. Automatic Control, vol. 44, no. 1, pp. 208–213, 1999.  Zbl1056.93542
  17. [17] J. A. Tenreiro Machado and A. M. S. Galhano, Statistical Fractional Dynamics, ASME Journal of Computational and Nonlinear Dynamics, vol. 3, no. 2, pp. 021201-1–021201-5, April 2008.  
  18. [18] Y. Q. Chen and K. L. Moore, Discretization schemes for fractional order differentiators and integrators, IEEE Trans. Circuits and Systems I Fundamental Theory and Applications, vol. 49, no. 3, pp. 363–367, 2002.  
  19. [19] B. M. Vinagre, Y. Q. Chen, and I. Petras, Two direct Tustin discretization methods for fractional-order differentiator/ integrator, Journal of The Franklin Institute, vol. 340, no. 5, pp. 349–362, 2003.  Zbl1051.93031
  20. [20] J. Van de Vegte, Fundamentals of Digital Signal Processing, Prentice Hall, 2003.  
  21. [21] W. T. Coffey, Yu. P. Kalmykov, and J. T. Waldron, The Langevin Equation, 2nd ed., World Scientific, Singapore, 2004.  Zbl1098.82001
  22. [22] S. C. Lim and S. V. Muniandy, Generalized Ornstein-Uhlenbeck processes and associated self-similar processes, Journal of Physics A: Mathematical and General, vol 36, no. 14, pp. 3961– 3982, 2003.  Zbl1083.60029
  23. [23] I. M. Gelfand and K. Vilenkin, Generalized Functions, Vol. 1, Academic Press, New York, 1964. (Translation in Chinese)  
  24. [24] A. Papoulis, The Fourier Integral and Its Applications, McGraw- Hill, 1962.  
  25. [25] M. Li and S. C. Lim, A rigorous derivation of power spectrum of fractional Gaussian noise, Fluctuation and Noise Letters, vol. 6, no. 4, pp. C33–C36, 2006. [Crossref] 
  26. [26] P. Hall and R. Roy, On the relationship between fractal dimension and fractal index for stationary stochastic processes, The Annals of Applied Probability, vol. 4, no. 1, pp. 241–253, 1994. [Crossref] Zbl0798.60035
  27. [27] M. Li, Fractal time series-a tutorial review,Mathematical Problems in Engineering, vol. 2010, 2010.  Zbl1191.37002
  28. [28] B. B. Mandelbrot, Gaussian Self-Aflnity and Fractals, Springer, 2001.  
  29. [29] J. Beran, Statistics for Long-Memory Processes, Chapman & Hall, 1994.  Zbl0869.60045
  30. [30] S. C. Lim, M. Li, and L. P. Teo, Locally self-similar fractional oscillator processes, Fluctuation and Noise Letters, vol. 7, no. 2, pp. L169–L179, 2007. [Crossref][WoS] 
  31. [31] M. Li and S. C. Lim, Modeling network traflc using generalized Cauchy process, Physica A, vol. 387, no. 11, pp. 2584–2594, 2008. [WoS] 
  32. [32] M. Li and W. Zhao, Detection of variations of local irregularity of traflc under DDOS flood attack, Mathematical Problems in Engineering, vol. 2008, 2008.  Zbl1189.68114
  33. [33] A. Kolmogorov, The local structure of turbulence in incompressible fluid for very large Reynolds numbers, Proc. Roy. Soc. London, Ser. A, vol. 434, no. 1890, pp. 9–13, 1991.  Zbl1142.76389

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