Applications of Genetic Algorithms in Realistic Wind Field Simulations

  • R. Montenegro
  • G. Montero
  • E. Rodríguez
  • J. M. Escobar
  • J. M. González-Yuste
Part of the Studies in Computational Intelligence book series (SCI, volume 102)

Mass consistent models have been widely use in 3-D wind modelling by finite element method. We have used a method for constructing tetrahedral meshes which are simultaneously adapted to the terrain orography and the roughness length by using a refinement/derefinement process in a 2-D mesh corresponding to the terrain surface, following the technique proposed in [14,15,18]. In this 2-D mesh we include a local refinement around several points which are previously defined by the user. Besides, we develop a technique for adapting the mesh to any contour that has an important role in the simulation, like shorelines or roughness length contours [3,4], and we refine the mesh locally for improving the numerical solution with the procedure proposed in [6].

This wind model introduces new aspects on that proposed in [16, 19, 20]. The characterization of the atmospheric stability is carried out by means of the experimental measures of the intensities of turbulence. On the other hand, since several measures are often available at a same vertical line, we have constructed a least square optimization of such measures for developing a vertical profile of wind velocities from an optimum friction velocity. Besides, the main parameters governing the model are estimated using genetic algorithms with a parallel implementation [12,20,26]. In order to test the model, some numerical experiments are presented, comparing the results with realistic measures.

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References

  1. 1.
    Bäck T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. Oxford Univ. Press, New York-OxfordMATHCrossRefGoogle Scholar
  2. 2.
    Davis L (1991) Handbook of genetic algorithms. Van Nostrand ReinholdGoogle Scholar
  3. 3.
    Escobar JM, Rodríguez E, Montenegro R, Montero G, González-Yuste JM (2003) Simultaneous untangling and smoothing of tetrahedral meshes. Comp Meth Appl Mech Eng 192:2775–2787MATHCrossRefGoogle Scholar
  4. 4.
    Escobar JM, Montero G, Montenegro R, Rodríguez E (2006) An algebraic method for smoothing surface triangulations on a local parametric space. Int J Num Meth Eng 66:740–760MATHCrossRefGoogle Scholar
  5. 5.
    Ferragut L, Montenegro R, Plaza A (1994) Efficient refinement/derefinement algorithm of nested meshes to solve evolution problems. Comm Num Meth Eng 10:403–412MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    González-Yuste JM, Montenegro R, Escobar JM, Montero G, Rodríguez E (2004) Local refinement of 3-D triangulations using object-oriented methods. Adv Eng Soft 35:693–702MATHCrossRefGoogle Scholar
  7. 7.
    Holland J (1992) Adaption in natural and artificial systems. MIT PressGoogle Scholar
  8. 8.
    Kitada T, Kaki A, Ueda H, Peters LK (1983) Estimation of vertical air motion from limited horizontal wind data - A numerical experiment. Atmos Environ 17:2181–2192CrossRefGoogle Scholar
  9. 9.
    Lalas DP, Tombrou M, Petrakis M (1988) Comparison of the performance of some numerical wind energy siting codes in rough terrain. In: European Community Wind Energy Conference, Herning, DenmarkGoogle Scholar
  10. 10.
    Lalas DP, Ratto CF (1996) Modelling of atmospheric flow fields. World Scientific Publishing, SingaporeGoogle Scholar
  11. 11.
    Levine D (1994) A Parallel Genetic Algorithm for the Set Partitioning Problem. PhD Thesis, Illinois Institute of Technology / Argonne National LaboratoryGoogle Scholar
  12. 12.
    Michalewicz Z (1994) Genetic algorithms + data structures = evolution problems. Springer Verlag, Berlin-Heidelberg-New YorkGoogle Scholar
  13. 13.
    Mikkelsen T (2003) Modelling of pollutant transport in the atmosphere. MANHAZ position paper, Ris∅ National Laboratory, DenmarkGoogle Scholar
  14. 14.
    Montenegro R, Montero G, Escobar JM, Rodríguez E, González-Yuste JM (2002) Tetrahedral mesh generation for environmental problems over complex terrain. Lect N Comp Sci 2329:335–344CrossRefGoogle Scholar
  15. 15.
    Montenegro R, Montero G, Escobar JM, Rodríguez E (2002) Efficient strategies for adaptive 3-D mesh generation over complex orography. Neural, Parallel & Scientific Computation 10:57–76MATHGoogle Scholar
  16. 16.
    Montero G, Montenegro R, Escobar JM (1998) A 3-D diagnostic model for wind field adjustment. J Wind Engrg Ind Aer 74-76:249–261CrossRefGoogle Scholar
  17. 17.
    Montero G, Sanin N (2001) Modelling of wind field adjustment using finite differences in a terrain conformal coordinate system. J Wind Engrg Ind Aer 89:471–488CrossRefGoogle Scholar
  18. 18.
    Montero G, Montenegro R, Escobar JM, Rodríguez (2003) Generación automática de mallas de tetraedros adaptadas a orografías irregulares. Rev Int Mét Num Cálc Dis Ing 19(2):127–144MATHGoogle Scholar
  19. 19.
    Montero G, Montenegro R, Escobar JM, Rodríguez E, González-Yuste JM (2004) Velocity field modelling for pollutant plume using 3-D adaptive finite element method. Lect N Comp Sci 3037:642–645Google Scholar
  20. 20.
    Montero G, Rodríguez E., Montenegro R, Escobar JM, González-Yuste JM (2005) Genetic algorithms for an improved parameter estimation with local refinenent of tetrahedral meshes in a wind model. Adv Engrg Soft 36:3–10MATHCrossRefGoogle Scholar
  21. 21.
    Moussiopoulos N, Flassak Th, Knittel G (1998) A refined diagnostic wind model. Environ Soft 3:85–94CrossRefGoogle Scholar
  22. 22.
    Pennel WT (1983) An Evaluation of the Role of Numerical Wind Field Models in Wind Turbine Siting. Batelle Memorial Institute, Pacific Northwest Laboratory, Richland, WashingtonGoogle Scholar
  23. 23.
    Pielke R (1984) Mesoscale meteorological modeling. Academic Press, Inc., Orlando, FloridaGoogle Scholar
  24. 24.
    Plaza A, Montenegro R, Ferragut L (1996) An improved derefinement algorithm of nested meshes. Adv Eng Soft 27:51–57CrossRefGoogle Scholar
  25. 25.
    Rivara MC (1987) A grid generator based on 4-triangles conforming. Mesh-refinement algorithms. Int J Num Meth Eng 24:1343–1354MATHCrossRefGoogle Scholar
  26. 26.
    Rodríguez E, Montero G, Montenegro R, Escobar JM, González- Yuste JM (2002) Parameter estimation in a three-dimensional wind field model using genetic algorithms. Lect Notes in Comp Sci 2329:950–959CrossRefGoogle Scholar
  27. 27.
    Seinfeld JH, Pandis SN (1998) Atmospheric chemistry and physics. From air pollution to climate change. John Wiley & Sons, Inc., New YorkGoogle Scholar
  28. 28.
    Spears W, DeJong K (1991) On the virtues of parametrized uniform crossover. In: Proceedings of the Fourth International Conference on Genetic AlgorithmsGoogle Scholar
  29. 29.
    Syswerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the Third International Conference on Genetic AlgorithmsGoogle Scholar
  30. 30.
    Vose M (1999) The simple genetic algorithm. MIT Press, Cambridge, MassachusettsMATHGoogle Scholar
  31. 31.
    Whitley D (1988) GENITOR: A different genetic algorithm. In: Rocky Mountain Conference on Artificial IntelligenceGoogle Scholar
  32. 32.
    Whitley D (1989) The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In: Proceedings of the Third International Conference on Genetic AlgorithmsGoogle Scholar
  33. 33.
    Winter G, Montero G, Ferragut L, Montenegro R (1995) Adaptive strategies using standard and mixed finite elements for wind field adjustment. Solar Energy 54:49-56CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • R. Montenegro
    • 1
  • G. Montero
    • 1
  • E. Rodríguez
    • 1
  • J. M. Escobar
    • 1
  • J. M. González-Yuste
    • 1
  1. 1.Institute for Intelligent Systems and Numerical Applications in EngineeringUniversity of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

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