Databases Coupling for Morphed-Mesh Simulations and Application on Fan Optimal Design

  • Zebin ZhangEmail author
  • Martin Buisson
  • Pascal Ferrand
  • Manuel Henner
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


Aerodynamic databases collected either by experimental or numerical approaches are relatively “local” in a large-scale design space, surrounding the reference configurations or operating conditions. However, the exploration of the design space requires knowledge of the “dark” space where few data is available. Therefore, the coupling of “remote” databases is necessary. Databases had been generated by performing CFD (Computational Fluid Dynamics) simulations with meshes morphed from different geometrical configurations. Then an ordinary least square method was used to obtain derivatives out of databases. Direct co-Kriging method was used to interpolate those derivative-integrated databases. Derivability studies were carried out on two main sub-models: regression model and correlation model. Appropriate models were proposed respectively. Referring to 2 geometries and 2 operating conditions, 4 second order integrated databases had been generated for an automotive engine cooling fan. Progressively database coupling shows the advantage of the proposed approach. Optimizations has been done to improve the fan performances at different operating conditions.


Database coupling Co-Kriging Optimal design CFD 


  1. 1.
    Constantine, P.G.: Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies, vol. 2. SIAM, Philadelphia, PA (2015)Google Scholar
  2. 2.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lecture Notes in Computer Science, vol. 1917. Springer, Berlin, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Forrester, A., Keane, A., Bresslo,NW.: Design and analysis of “noisy” computer experiments[J]. AIAA J. 44(10), 2331–2339 (2012)Google Scholar
  4. 4.
    Wang, G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. ASME J. Mech. Des. 129(4), 370–380 (2007)CrossRefGoogle Scholar
  5. 5.
    Giraldo, R., Dabo-Niang, S.: Statistical modeling of spatial big data: an approach from a functional data analysis perspective. Stat. Prob. Lett. (2018) (in press)Google Scholar
  6. 6.
    Han, Z., Zimmerman, R., Görtz, S.: Alternative cokriging model for variable-fidelity surrogate modeling. AIAA J. 50(5), 1205–1210 (2012)Google Scholar
  7. 7.
    Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. J. Global Optim. 21(4), 345–383 (2001). Scholar
  8. 8.
    Krige, D.: Statistical approach to some mine valuations and allied problems at the witwatersrand. Master’s thesis, University of Witwatersrand (1951)Google Scholar
  9. 9.
    Laurenceau, J., Meaux, M., Montagnac, M., Sagaut, P.: Comparison of gradient-based and gradient-enhanced response-surface-based optimizers. AIAA J. 48(5), 981–994 (2010)CrossRefGoogle Scholar
  10. 10.
    Leifsson, L., Koziel, S., Tesfahunegn, Y.A.: Multiobjective aerodynamic optimization by variable-fidelity models and response surface surrogates. AIAA J. 54(2), 531–541 (2016)CrossRefGoogle Scholar
  11. 11.
    Lophaven, S.N.: Aspects of the matlab toolbox dace. Technical Report, University of Denmark (2002)Google Scholar
  12. 12.
    March, A., Willcox, K.: Provably convergent multifidelity optimization algorithm not requiring high-fidelity derivatives. AIAA J. 50(5), 1079–1089 (2012)CrossRefGoogle Scholar
  13. 13.
    Matheron, G.: Principles of geostatistics. Econ. Geol. 58, 1246–1266 (1963)CrossRefGoogle Scholar
  14. 14.
    Probst, D.M., Senecal, P.K.: Optimization and uncertainty analysis of a diesel engine operating point using computational fluid dynamics. ASME 2016 Internal Combustion Engine Division Fall Technical Conference, Greenville, South Carolina, USA (2016)Google Scholar
  15. 15.
    Rendall, T.C.S., Allen, C.B.: Unified fluid-structure interpolation and mesh motion using radial basis functions. Int. J. Numer. Methods Eng. 74, 1519–1559 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Rozenberg, Y., Benefice, G., Aubert, S.: Fluid structure interaction problems in turbomachinery using rbf interpolation and greedy algorithm. In: ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, vol. 16, no. 1, p. 102 (2014)Google Scholar
  17. 17.
    Rumpfkeil, M.P.: Optimizations under uncertainty using gradients, hessians, and surrogate models. AIAA J. 51(2), 444–451 (2013)CrossRefGoogle Scholar
  18. 18.
    Schnoes, M., Nicke, E.: A database of optimal airfoils for axial compressor throughflow design. ASME J. Turbomach. 139(5) (2017)CrossRefGoogle Scholar
  19. 19.
    Villemonteix, J., Vazquez, E., Walter, E.: An informational approach to the global optimization of expensive-to-evaluate functions. J. Global Optim. 44(4), 509–534 (2008)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Yamazaki, W., Mavriplis, D.J.: Derivative-enhanced variable fidelity surrogate modeling for aerodynamic functions. AIAA J. 51(1), 126–137 (2013)CrossRefGoogle Scholar
  21. 21.
    Zhang, Z., Demory, B.: Space infill study of kriging meta-model for multi-objective optimization of an engine cooling fan. Turbine Technical Conference and Exposition. In: Proceedings of ASME Turbo Expo 2014, Dusseldorf, Germany (2014)Google Scholar
  22. 22.
    Zhang, Z., Buisson, M., Ferrand, P.: Meta-model based optimization of a large diameter semi-radial conical hub engine cooling fan. Mech. Ind. 16(1), 102 (2015)CrossRefGoogle Scholar
  23. 23.
    Zhang, Z., Han, Z., Ferrand, P.: High anisotropy space exploration with co-kriging method. Global Optimization Workshops 2018 (LeGO). Leiden, Netherlands (2018)Google Scholar
  24. 24.
    Zhao, L., Choi, K.K., Lee, I.: Metamodeling method using dynamic kriging for design optimization. AIAA J. 49(9), 2034–2046 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zebin Zhang
    • 1
    Email author
  • Martin Buisson
    • 2
  • Pascal Ferrand
    • 2
  • Manuel Henner
    • 3
  1. 1.Zhengzhou UniversityZhengzhouChina
  2. 2.Ecole Centrale de LyonEcullyFrance
  3. 3.Valeo Thermal SystemsLa VerrièreFrance

Personalised recommendations