Abstract
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.
Supported by NSFC No.51575498.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Constantine, P.G.: Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies, vol. 2. SIAM, Philadelphia, PA (2015)
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)
Forrester, A., Keane, A., Bresslo,NW.: Design and analysis of “noisy” computer experiments[J]. AIAA J. 44(10), 2331–2339 (2012)
Wang, G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. ASME J. Mech. Des. 129(4), 370–380 (2007)
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)
Han, Z., Zimmerman, R., Görtz, S.: Alternative cokriging model for variable-fidelity surrogate modeling. AIAA J. 50(5), 1205–1210 (2012)
Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. J. Global Optim. 21(4), 345–383 (2001). https://doi.org/10.1023/A:1012771025575
Krige, D.: Statistical approach to some mine valuations and allied problems at the witwatersrand. Master’s thesis, University of Witwatersrand (1951)
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)
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)
Lophaven, S.N.: Aspects of the matlab toolbox dace. Technical Report, University of Denmark (2002)
March, A., Willcox, K.: Provably convergent multifidelity optimization algorithm not requiring high-fidelity derivatives. AIAA J. 50(5), 1079–1089 (2012)
Matheron, G.: Principles of geostatistics. Econ. Geol. 58, 1246–1266 (1963)
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)
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)
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)
Rumpfkeil, M.P.: Optimizations under uncertainty using gradients, hessians, and surrogate models. AIAA J. 51(2), 444–451 (2013)
Schnoes, M., Nicke, E.: A database of optimal airfoils for axial compressor throughflow design. ASME J. Turbomach. 139(5) (2017)
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)
Yamazaki, W., Mavriplis, D.J.: Derivative-enhanced variable fidelity surrogate modeling for aerodynamic functions. AIAA J. 51(1), 126–137 (2013)
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)
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)
Zhang, Z., Han, Z., Ferrand, P.: High anisotropy space exploration with co-kriging method. Global Optimization Workshops 2018 (LeGO). Leiden, Netherlands (2018)
Zhao, L., Choi, K.K., Lee, I.: Metamodeling method using dynamic kriging for design optimization. AIAA J. 49(9), 2034–2046 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Z., Buisson, M., Ferrand, P., Henner, M. (2020). Databases Coupling for Morphed-Mesh Simulations and Application on Fan Optimal Design. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_97
Download citation
DOI: https://doi.org/10.1007/978-3-030-21803-4_97
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21802-7
Online ISBN: 978-3-030-21803-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)