Journal of Zhejiang University SCIENCE A

, Volume 13, Issue 4, pp 274–283 | Cite as

A computational fluid dynamics model for wind simulation: model implementation and experimental validation

  • Zhuo-dong Zhang
  • Ralf Wieland
  • Matthias Reiche
  • Roger Funk
  • Carsten Hoffmann
  • Yong Li
  • Michael Sommer


To provide physically based wind modelling for wind erosion research at regional scale, a 3D computational fluid dynamics (CFD) wind model was developed. The model was programmed in C language based on the Navier-Stokes equations, and it is freely available as open source. Integrated with the spatial analysis and modelling tool (SAMT), the wind model has convenient input preparation and powerful output visualization. To validate the wind model, a series of experiments was conducted in a wind tunnel. A blocking inflow experiment was designed to test the performance of the model on simulation of basic fluid processes. A round obstacle experiment was designed to check if the model could simulate the influences of the obstacle on wind field. Results show that measured and simulated wind fields have high correlations, and the wind model can simulate both the basic processes of the wind and the influences of the obstacle on the wind field. These results show the high reliability of the wind model. A digital elevation model (DEM) of an area (3800 m long and 1700 m wide) in the Xilingele grassland in Inner Mongolia (autonomous region, China) was applied to the model, and a 3D wind field has been successfully generated. The clear implementation of the model and the adequate validation by wind tunnel experiments laid a solid foundation for the prediction and assessment of wind erosion at regional scale.

Key words

Wind model Computational fluid dynamics (CFD) Wind erosion Wind tunnel experiments Spatial analysis and modelling tool (SAMT) Open source 

CLC number

O368 S157.1 


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  1. Alhajraf, S., 2004. Computational fluid dynamic modelling of drifting particles at porous fences. Environmental Modelling & Software, 19(2):163–170. [doi:10.1016/S1364-8152(03)00118-X]CrossRefGoogle Scholar
  2. Badr, T., Harion, J.L., 2005. Numerical modelling of flow over stockpiles: Implications on dust emissions. Atmospheric Environment, 39(30):5576–5584. [doi:10.1016/j.atmosenv. 2005.05.053]CrossRefGoogle Scholar
  3. Gao, X., Huo, W., Luo, Z.Y., Cen, K.F., 2008. CFD simulation with enhancement factor of sulfur dioxide absorption in the spray scrubber. Journal of Zhejiang University-SCIENCE A, 9(11):1601–1613. [doi:10.1631/jzus. A0820507]MATHCrossRefGoogle Scholar
  4. Gill, T.E., Shao, Y.P., 2004. Introduction: Modelling of wind erosion and aeolian processes. Environmental Modelling & Software, 19(2):91–92. [doi:10.1016/S1364-8152(03) 00112-9]CrossRefGoogle Scholar
  5. Gray, G.A., Kolda, T.G., 2006. Algorithm 856: APPSPACK 4.0: Asynchronous parallel pattern search for derivative-free optimization. ACM Transactions on Mathematical Software, 32(3):485–507. [doi:10.1145/1163641.1163647]MathSciNetMATHCrossRefGoogle Scholar
  6. Griffin, J.D., Kolda, T.G., 2006. Asynchronous Parallel Ge-nerating Set Search for Linear-Constrained Optimization. Technical Report, Sandia National Laboratories, Livermore.Google Scholar
  7. Hoffmann, C., Wieland, R., Funk, R., 2007. Mapping the Soil Erodibility of a Grazing Area in Inner Mongolia Using a Fuzzy Development Tool (SAMT). In: Wittmann, J., Wohlgemuth, V. (Eds.), Simulation in Umwelt-und Geowissenschaften: Workshop Berlin. Aachen (Shaker), p.71–80 (in German).Google Scholar
  8. Hoffmann, C., Funk, R., Wieland, R., Li, Y., Sommer, M., 2008. Effects of grazing and topography on dust flux and deposition in the Xilingele grassland, Inner Mongolia. Journal of Arid Environments, 72(5):792–807. [doi:10. 1016/j.jaridenv.2007.09.004]CrossRefGoogle Scholar
  9. Hussein, A.S., El-Shishiny, H., 2009. Influences of wind flow over heritage sites: A case study of the wind environment over the Giza Plateau in Egypt. Environmental Modelling & Software, 24(3):389–410. [doi:10.1016/j.envsoft.2008. 08.002]CrossRefGoogle Scholar
  10. Kolda, T.G., 2005. Revisiting asynchronous parallel pattern search for nonlinear optimization. SIAM Journal of Optimization, 16(2):563–586. [doi:10.1137/040603589]MathSciNetMATHCrossRefGoogle Scholar
  11. Liu, C.H., Leung, D.Y.C., Man, A.C.S, Chan, P.W., 2010. Computational fluid dynamics simulation of the wind flow over an airport terminal building. Journal of Zhejiang University-SCIENCE A (Applied Physics and Engineering), 11(6):389–401. [doi:10.1631/jzus.A0900449]MATHCrossRefGoogle Scholar
  12. Mirschel, W., Wieland, R., Voss, M., Ajibefun, I.A., Deumlich, D., 2006. Spatial analysis and modelling tool (SAMT): 2. Applications. Ecological Informatics, 1(1):77–85. [doi:10. 1016/j.ecoinf.2005.10.004]CrossRefGoogle Scholar
  13. Parsons, D.R., Wiggs, G.F.S., Walker, I.J., Ferguson, R.I., Garvey, B.G., 2004a. Numerical modelling of airflow over an idealised transverse dune. Environmental Modelling & Software, 19(2):153–162. [doi:10.1016/S1364-8152(03) 00117-8]CrossRefGoogle Scholar
  14. Parsons, D.R., Walker, I.J., Wiggs, G.F.S., 2004b. Numerical modelling of flow structures over idealized transverse aeolian dunes of varying geometry. Geomorphology, 59(1–4):149–164. [doi:10.1016/j.geomorph.2003.09.012]CrossRefGoogle Scholar
  15. Ross, A.N., Arnold, S., Vosper, S.B., Mobbs, S.D., Dixon, N., Robins, A.G., 2004. A comparison of wind-tunnel experiments and numerical simulations of neutral and stratified flow over a hill. Boundary-Layer Meteorology, 113(3):427–459. [doi:10.1007/s10546-004-0490-z]CrossRefGoogle Scholar
  16. Seleznev, V., 2007. Numerical simulation of a gas pipeline network using computational fluid dynamics simulators. Journal of Zhejiang University-SCIENCE A, 8(5): 755–765. [doi:10.1631/jzus.2007.A0755]MATHCrossRefGoogle Scholar
  17. Shi, F., Huang, N., 2010. Computational simulations of blown sand fluxes over the surfaces of complex microtopography. Environmental Modelling & Software, 25(3):362–367. [doi:10.1016/j.envsoft.2009.09.002]CrossRefGoogle Scholar
  18. Solazzo, E., Cai, X.M., Vardoulakis, S., 2009. Improved parameterisation for the numerical modelling of air pollution within an urban street canyon. Environmental Modelling & Software, 24(3):381–388. [doi:10.1016/j.envsoft. 2008.08.001]CrossRefGoogle Scholar
  19. Stam, J., 2003. Real-Time Fluid Dynamics for Games. Proceedings of the Game Developer Conference. Available from: [Accessed on Oct. 16, 2008].
  20. Wakes, S.J., Maegli, T., Dickinson, K.J., Hilton, M.J., 2010. Numerical modelling of wind flow over a complex topography. Environmental Modelling & Software, 25(2): 237–247. [doi:10.1016/j.envsoft.2009.08.003]CrossRefGoogle Scholar
  21. Wieland, R., Voss, M., Holtmann, X., Mirschel, W., Ajibefun, I.A., 2006. Spatial analysis and modelling tool (SAMT): 1. Structure and possibilities. Ecological Informatics, 1(1): 67–76. [doi:10.1016/j.ecoinf.2005.10.005]CrossRefGoogle Scholar
  22. Wieland, R., Mirschel, W., Wenkel, K.O., 2007. Spatial Analysis and Modelling Tool V2.0-System Design. In: Gnauck, A. (Ed.), Modellierung und Simulation von Oekosystemen: Workshop Koelpinsee. Aachen (Shaker), p.78–96 (in German).Google Scholar
  23. Yang, Y., Shao, Y.P., 2008. Numerical simulations of flow and pollution dispersion in urban atmospheric boundary layers. Environmental Modelling & Software, 23(7):906–921. [doi:10.1016/j.envsoft.2007.10.005]CrossRefGoogle Scholar
  24. Youssef, F., Visser, S., Karssenberg, D., Bruggeman, A., Erpul, G., 2012. Calibration of RWEQ in a patchy landscape; a first step towards a regional scale wind erosion model. Aeolian Research, 3(4):467–476. [doi:10.1016/j.aeolia. 2011.03.009]CrossRefGoogle Scholar
  25. Zhang, Z.D., Wieland, R., Reiche, M., Funk, R., Hoffmann, C., Li, Y., Sommer, M., 2011. Wind modelling for wind erosion research by open source computational fluid dynamics. Ecological Informatics, 6(5):316–324. [doi:10.1016/ j.ecoinf.2011.02.001]CrossRefGoogle Scholar
  26. Zobeck, T.M., Parker, N.C., Haskell, S., Guoding, K., 2000. Scaling up from field to region for wind erosion prediction using a field-scale wind erosion model and GIS. Agriculture Ecosystems & Environment, 82(1–3):247–259. [doi:10.1016/S0167-8809(00)00229-2]CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhuo-dong Zhang
    • 1
  • Ralf Wieland
    • 2
  • Matthias Reiche
    • 1
  • Roger Funk
    • 1
  • Carsten Hoffmann
    • 1
  • Yong Li
    • 3
  • Michael Sommer
    • 1
    • 4
  1. 1.Institute of Soil Landscape ResearchLeibniz-Centre for Agricultural Landscape Research (ZALF)MuenchebergGermany
  2. 2.Institute of Landscape System AnalysisLeibniz-Centre for Agricultural Landscape Research (ZALF)MuenchebergGermany
  3. 3.Institute of Agricultural Environment and Sustainable DevelopmentChinese Academy of Agricultural Sciences (CAAS)BeijingChina
  4. 4.Institute of Earth and Environmental ScienceUniversity of PotsdamPotsdamGermany

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