Advertisement

Adaptive Sampling Strategies for Surrogate-Based Aerodynamic Optimization

  • Emiliano IulianoEmail author
Part of the Springer Tracts in Mechanical Engineering book series (STME)

Abstract

The chapter proposes the application of surrogate-based optimization to the efficient design of aeronautical configurations. The surrogate model consists of the Proper Orthogonal Decomposition of computed aerodynamic flow fields and Radial Basis Functions interpolation to reconstruct the aerodynamic flow at any unknown design vector. The surrogate model is coupled to an evolutionary algorithm to globally explore the design space. Several adaptive sampling strategies are proposed, either objective-driven (i.e. aimed at improving the fitness function) or error-driven (i.e. aimed at reducing the prediction error of the surrogate model globally). The proposed methodology is applied to the design optimization of a two-dimensional airfoil in multi-point transonic conditions. The results of different training strategies are critically discussed and compared.

Keywords

Design Space Proper Orthogonal Decomposition Proper Orthogonal Decomposition Mode Kriging Model Adaptive Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The author thanks Dr. Esther Andres Perez and Dr. Mario J. Martin-Burgos from Instituto Nacional de Tcnica Aeroespacial (INTA), Spain, for providing the NURBS parameterization tool and supporting its integration. The development and experiments presented in the paper have been partially achieved within the AG52 GARTEUR action group (http://ag52.blogspot.it/, www.garteur.org) that has been established to explore these surrogate-based global approaches. The main objective of the action group is, by means of a European collaborative research, to make a deep evaluation and assessment of surrogate-based global optimization methods for aerodynamic shape design, dealing with the main challenges as the curse of dimensionality, reduction of the design space and error metrics for validation, amongst others.

References

  1. 1.
    Amato M, Catalano P (2000) Non linear κ \(\varepsilon\) turbulence modeling for industrial applications. In: ICAS 2000 congress. IOS Press, HarrogateGoogle Scholar
  2. 2.
    Chandrashekarappa P, Duvigneau R (2007) Radial basis functions and Kriging metamodels for aerodynamic optimization. Rapport de recherche RR-6151, INRIA. http://hal.inria.fr/inria-00137602/en/
  3. 3.
    Cook PH, McDonald MA, Firmin MCP (1979) Aerofoil rae 2822—pressure distributions, boundary layer and wake measurements. In: Experimental data base for computer program assessment. AGARD. AGARD AR-138, Paper A6Google Scholar
  4. 4.
    Forrester AIJ, Keane AJ (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1–3):50–79. http://dx.doi.org/10.1016/j.paerosci.2008.11.001
  5. 5.
    Gutmann HM (2001) A radial basis function method for global optimization. J Glob Optim 19:201–227. doi:10.1023/A:1011255519438. http://dl.acm.org/citation.cfm?id=596093.596381
  6. 6.
    Iuliano E (2011) Towards a POD-based surrogate model for CFD optimization. In: Proceedings of the ECCOMAS CFD & optimization conference, AntalyaGoogle Scholar
  7. 7.
    Iuliano E, Quagliarella D (2011) Surrogate-based aerodynamic optimization via a zonal pod model. In: Proceedings of the EUROGEN 2011 conference, CapuaGoogle Scholar
  8. 8.
    Iuliano E, Quagliarella D (2013) Aerodynamic shape optimization via non-intrusive pod-based surrogate modelling. Comput Fluids 84:327–350zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Iuliano E, Quagliarella D (2013) Aerodynamic shape optimization via non-intrusive pod-based surrogate modelling. In: Proceedings of 2013 IEEE CEC congress on evolutionary computationGoogle Scholar
  10. 10.
    Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13:455–492. doi:10.1023/A:1008306431147. http://dx.doi.org/10.1023/A:1008306431147
  11. 11.
    Queipo N, Haftka R, Shyy W, Goel T, Vaidyanathan R, Kevintucker P (2005) Surrogate-based analysis and optimization. Prog Aerosp Sci 41(1):1–28. http://linkinghub.elsevier.com/retrieve/pii/S0376042105000102
  12. 12.
    Schonlau M, Welch WJ, Jones DR (1998) Global versus local search in constrained optimization of computer models. Lecture notes-monograph series, vol 34. doi:10.2307/4356058. http://dx.doi.org/10.2307/4356058
  13. 13.
    Simpson TW, Toropov VV, Balabanov V, Viana FAC (2008) Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come—or not. In: Proceedings of the 12th AIAA/ISSMO multidisciplinary analysis and optimization conference, AIAA 2008-5802. American Institute of Aeronautics and Astronautics, pp 1–22Google Scholar
  14. 14.
    Sóbester A, Leary S, Keane A (2004) A parallel updating scheme for approximating and optimizing high fidelity computer simulations. Struct Multidiscip Optim 27:371–383. doi:10.1007/s00158-004-0397-9. http://dx.doi.org/10.1007/s00158-004-0397-9.
  15. 15.
    Vitagliano PL, Quagliarella D (2003) A hybrid genetic algorithm for constrained design of wing and wing-body configurations. In: Bugeda G, Désidéri JA, Périaux J, Schoenauer M, Winter G (eds) Evolutionary methods for design, optimization and control applications to industrial and societal problems. International Center for Numerical Methods in Engineering (CIMNE), BarcelonaGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CIRAThe Italian Aerospace Research CenterCapuaItaly

Personalised recommendations