Abstract
The development of an automatic geometry optimization tool for efficient aerodynamic shape design, supported by Computational Fluid Dynamic (CFD) methods is nowadays an attractive research field, as can be observed from the increasing number of scientific publications during the last years. Surrogate-based global optimization methods have demonstrated a huge potential to reduce the actual number of CFD runs, and therefore drastically speed-up the design process. Nevertheless, surrogates need initial high fidelity data sets to be built and to reach a proper accuracy. This work presents a study on the influence of the initial training dataset size in the proposed approach behavior. This approach is based on the use of Support Vector Machines (SVMs) as the surrogate model for estimating the objective function, in combination with an Evolutionary Algorithm (EA) and an adaptive sampling technique focused on optimization called the Intelligent Estimation Search with Sequential Learning (IES-SL). Several number of training points have been fixed to check the convergence, the accuracy and the objective function reached by the method.
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References
Andrés E, Salcedo-Sanz S, Mongue F, Pérez-Bellido A (2012) Efficent aerodynamic design through evolutionary programming and support vector regression algorithms. Expert Syst Appl 39:10700–10708
Andrés-Pérez E, Iuliano E (2015) Application of surrogate-based global optimization to aerodynamic design, Springer Tracts in Mechanical Engineering
Andrés-Pérez E, González-Juárez D, Martin-Burgos M, Carro-Calvo L, Salcedo-Sanz S (2016) Influence of the number and location of design parameters in the aerodynamic shape optimization of a transonic aerofoil and a wing through evolutionary algorithms and support vector machines. Eng Optim 48
Cook P, Mcdoland M, Firmin M (1979) Aerofoil RAE 2822 - Pressure Distributions, and Boundary Layer and Wake Measurements. AGARD Report 138
ESDU (1973) Second-order method for estimating the subcritical pressure distribution on a two-dimensional aerofoil in compressible inviscid flow
González-Juárez D, Andrés-Pérez E, Martin-Burgos M, Carro-Calvo L, Salcedo-Sanz S (2015) Influence of geometry parameterization in aerodynamic shape design of aeronautical configurations by evolutionary algorithms. In: 6th European conference for aeronautics and space sciences (EUCASS). Krakow, Poland
Iuliano E, Quagliarella D (2013) Aerodynamic shape optimization via non-intrusive POD-based surrogate modeing. In: IEEE congress on evolutionary computation. Cancún, Mexico
Jahangirian A, Shahrokhi A (2011) Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver. Comput Fluids 46:270–276
Keane A (2003) Wing optimization using design of experiment, response surface, and data fusion methods. J Aircr 40(4):741–750
Koziel S, Leifsson L (2013) Multi-level surrogate-based airfoil shape optimization. In: 51st AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, grapevine (Dallas/Ft. Worth Region), Texas
Leifsson L, Koziel S, Tesfahungen Y (2014) Aerodynamic design optimization: physics-based surrogate approaches for airfoil and wing design. In: AIAA SciTech
Li C, Brezillon J, Görtz S (2001) A framework for surrogate-based aerodynamic optimization. In: ONERA-DLR aero-space symposium, ODAS
Likeng H, Zhenghong G (2012) Wing-body optimization based on multi-fidelity surrogate model. In: International congress of the aeronautical sciences ICAS. Brisbane, Australia
Lukaczyk T, Palacios F, Alonso J (2014) Active subspaces for shape optimization. In: AIAA SciTech
Martin M, Andrés E, Valero E, Lozano C (2013) Gradients calculation for arbitrary parameterizations via volumetric NURBS: the control box approach. In: EUCASS
Mousavi A, Castonguay P, Nadarajah S (2007) Survey of shape parameterization techniques and its effect on three-dimensional aerodynamic shape optimization. In: AIAA computational fluid dynamics. Miami
Ortiz-García E, Sanz SS, Pérez-Bellido ÁM, Portilla-Figueras JA (2009) Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions. Neurocomputing
Parr J, Holden C, Forrester A, Keane A (2010) Review of efficient surrogate infill sampling criteria with constraint handling. In: 2nd international conference on engineering optimization. Lisbon, Portugal
Piegl L, Tiller W (1997) The NURBS book. Springer, Berlin
Acknowledgements
The research described in this work has been supported under INTA activity “Termofluidodinámica” (IGB99001) and the ‘Rafael Calvo Rodés’ scholarship.
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González-Juarez, D., Andrés-Pérez, E. (2019). Study of the Influence of the Initial a Priori Training Dataset Size in the Efficiency and Convergence of Surrogate-Based Evolutionary Optimization. In: Andrés-Pérez, E., González, L., Periaux, J., Gauger, N., Quagliarella, D., Giannakoglou, K. (eds) Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-89890-2_12
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DOI: https://doi.org/10.1007/978-3-319-89890-2_12
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