Improving Correlation Function Method to Generate Three-Dimensional Atmospheric Turbulence

  • Lianlei LinEmail author
  • Kun Yan
  • Jiapeng Li
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)


Atmospheric turbulence is a common form of wind field that causes turbulence for aircraft. A high-intensity turbulence field may negatively affect flight safety. With the development of simulation modeling and software engineering, the influence of the atmospheric turbulence on an aircraft has been widely studied using simulation experiments. Because the method for generating one-dimensional atmospheric turbulence is now mature, researchers have been confronted with a growing need to generate the three-dimensional atmospheric turbulence field that is required in the new simulation experiments. In the current study, we generate a three-dimensional atmospheric turbulence field based on an improved correlation function method. The main innovation is that we use the double random switching algorithm to adapt the Gaussian white noise sequence that is closer to the ideal condition when creating the one-dimensional atmospheric turbulence field. The two-dimensional and the final three-dimensional atmospheric turbulence field can be generated based on the one-dimensional one by iteration. There are experimental results to confirm that the three-dimensional atmospheric turbulence generated by this method provides improved transverse and longitudinal correlations as well as reduced error when compared with the theoretical values.


Atmospheric turbulence Three dimensional Correlation function 



This work is supported by the National Science Foundation of China under Grant No. 61201305.


  1. 1.
    Real, T.R.: Digital simulation of atmospheric turbulence for Dryden and von Karman models. J. Guid. Control Dyn. 16(1), 132–138 (1993)CrossRefGoogle Scholar
  2. 2.
    Reeves, P.M.: A non-Gaussian turbulence simulation. Air Force Flight Dynamics Lab Technical Report AFFDL-TR-69-67, Wright-Patterson Air Force Base, OH, Nov 1969Google Scholar
  3. 3.
    Fichtl, G.H., Perlmutter, M.: Nonstationary atmospheric boundary-layer turbulence simulation. J. Aircr. 1(12), 639–647 (1975)CrossRefGoogle Scholar
  4. 4.
    Zhao, Z.Y., et al.: Dryden digital simulation on atmospheric turbulence. Acta Aeronaut. Astronaut. Sin. 10, 7(5), 433–443Google Scholar
  5. 5.
    Ma, D.L., et al.: An improved method for digital simulation of atmospheric turbulence. J. Beijing Univ. Aeronaut. Astronaut. 3, 57–63 (1990)Google Scholar
  6. 6.
    Djurovic, Z., Miskovic, L., Kovacevic, B.: Simulation of air turbulence signal and its application. In: The 10th Mediterranean Electrotechnical Conference, vol. 1(2), pp. 847–850 (2000)Google Scholar
  7. 7.
    Zhang, F., et al.: Simulation of three-dimensional atmospheric turbulence based on Von Karman model. Comput. Stimul. 24(1), 35–38 (2007)Google Scholar
  8. 8.
    Xiao, Y.L.: Digital generation method for two-dimensional turbulent flow field in flight simulation. Acta Aeronaut. Astronaut. Sin. 11(4), B124–B130 (1990)Google Scholar
  9. 9.
    Lu, Y.P., et al.: Digital generation of two-dimensional field of turbulence based on spatial correlation function. J. Nanjing Univ. Aeronaut. Astronaut. 31(2), 139–145 (1999)Google Scholar
  10. 10.
    Hong, G.X., et al.: Monte Carlo stimulation for 3D-field of atmospheric turbulence. Acta Aeronaut. Astronaut. Sin. 22(6), 542–545 (2001)Google Scholar
  11. 11.
    Gao, J., et al.: Theory and method of numerical simulation for 3D atmospheric turbulence field based on Von Karman model. J. Beijing Univ. Aeronaut. Astronaut. 38(6), 736–740 (2012)Google Scholar
  12. 12.
    Gao, Z.X., et al.: Generation and extension methods of 3D atmospheric turbulence field. J. Traffic Transp. Eng. 8(4), 25–29 (2008)Google Scholar
  13. 13.
    Wu, Y., Jiang, S., Lin, L., Wang, C.: Simulation method for three-dimensional atmospheric turbulence in virtual test. J. Comput. Inf. Syst. 7(4), 1021–1028 (2011). Proctor, F.H., Bowles, R.L.: Three-dimensional simulation of the Denver 11 July 1988 Microburst-producing storm. Meteorol. Atmos. Phys. 49, 108–127 (1992)Google Scholar
  14. 14.
    Hunter, I.W., Kearney, R.E.: Generation of random sequences with jointly specified probability density and autocorrelation functions. Biol. Cybern. 47, 141–146 (1983)zbMATHCrossRefGoogle Scholar
  15. 15.
    Cai, K.B., et al.: A novel method for generating Gaussian stochastic sequences. J. Shanghai Jiaotong Univ. 38(12), 2052–2055 (2004)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.Beijing Near Space Airship Technology Development Co., LtdBeijingChina

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