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Abstract

This work presents a summary of the results obtained during the activities developed within the GARTEUR AD/AG-52 group. GARTEUR stands for “Group for Aeronautical Research and Technology in Europe” and is a multinational organization that performs high quality, collaborative, precompetitive research in the field of aeronautics to improve technological competence of the European Aerospace Industry. The aim of the AG52 group was to make an evaluation and assessment of surrogate-based global optimization methods for aerodynamic shape design of aeronautical configurations. The structure of the paper is as follows: Sect. “Introduction” will introduce the state-of-the-art in surrogate-based optimization for aerodynamic design and Sect. “Definition of Common Test Cases and Methods” will detail the test cases selected in the AG52 group. Optimization results will be then showed in Sect. “Optimization Results”, and conclusions will be provided in the last section.

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References

  • Ahmed M, Qin N (2010) Metamodels for aerothermodynamic design optimization. In: 48th AIAA aerospace sciences meeting. AIAA 2010-1318

    Google Scholar 

  • AIAA 3rd drag prediction workshop. URL: http://aaac.larc.nasa.gov/tsab/cfdlarc/aiaa-dpw/Workshop3/workshop3.html, [Online]

  • Andres E, Monge F (INTA), Perez A, Salcedo S (UAH) (2011) Metamodel assisted aerodynamic design using evolutionary optimization. EUROGEN

    Google Scholar 

  • Asouti VG, Giannakoglou KC (Technical University of Athens, Greece) (2009) A grid-27 asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization. Genetic Programming and Evolvable Machines

    Google Scholar 

  • Bompard B, Peter J (ONERA, France), Desideri J (INRIA, France) (2010) Surrogate models based on function and derivative values for aerodynamic global optimization. ECOMAS CFD

    Google Scholar 

  • Branke J, Schmidt C (2005) Faster convergence by means of fitness estimation. Soft Comput Fusion Found Methodologies Appl 9(1):13–20

    Google Scholar 

  • Buche D, Schraudolph N, Koumoutsakos P (2005) Accelerating evolutionary algorithms with Gaussian process fitness function models. IEEE Trans Syst Man Cybern Part C Appl Rev 35:183–194

    Article  Google Scholar 

  • Carrier G (2006) Single and multipoint aerodynamic optimizations of a supersonic transport aircraft wing using optimization strategies involving adjoint method and genetic algorithm. In: ERCOFTAC conference on optimization

    Google Scholar 

  • Cheng CS, Chen PW, Huang KK (2011) Estimating the shift size in the process mean with support vector regression and neural networks. Expert Syst Appl 38:10624–10630

    Article  Google Scholar 

  • Clarke SM, Griebsch JH, Simpson TW (2005) Analysis of support vector regression for approximation of complex engineering analyses. Trans ASME, J Mech Des 127(6):1077–1087

    Article  Google Scholar 

  • Cook P, Mcdoland M, Firmin M (1979) Aerofoil RAE 2822—pressure distributions, and boundary layer and wake measurements. AGARD Report 138

    Google Scholar 

  • de Weerdt E, Chu QP, Mulder JA (Delft University of Technology, The Netherlands) (2005) Neural network aerodynamic model identification for aerospace reconfiguration. AIAA

    Google Scholar 

  • Duvigneau R, Visonneau M (2004) (Ecole Centrale de Nantes, France), Hybrid genetic algorithms and artificial neural networks for complex design optimization in CFD, Int J Numer Methods Fluids 44

    Google Scholar 

  • Epstein B, Jameson A, Peigin S, Roman D, Harrison N, Vassberg J (2008) Comparative study of 3D wing drag minimization by different optimization techniques. In: 46th AIAA aerospace sciences meeting and exhibit. AIAA paper 2, Reno, Nevada

    Google Scholar 

  • Forrester A, Keane A (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1–3):50–79

    Article  Google Scholar 

  • Giannakoglou K (2002) Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence. Prog Aerosp Sci 38(1):43–76

    Article  Google Scholar 

  • Iuliano E, Andrés E (2015) “Application of surrogate-based global optimization to aerodynamic design.” Springer Tracts in Mechanical Engineering. ISBN 978-3-319-21505-1

    Google Scholar 

  • Iuliano E, Quagliarella D (2013) “Aerodynamic shape optimization via non-intrusive POD-based surrogate modelling.” In: IEEE congress on evolutionary computation. Cancún, México, June 20–23

    Google Scholar 

  • Jiang H, He W (2012) Grey relational grade in local support vector regression for financial time series prediction. Expert Syst Appl 39:2256–2262

    Article  Google Scholar 

  • Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput J 9(1):3–12

    Article  MathSciNet  Google Scholar 

  • Jin Y, Olhofer M, Sendhoff B (2002) A framework for evolutionary optimization with approximate fitness functions. IEEE Trans Evol Comput 6(5):481–494

    Google Scholar 

  • Kampolis IC, Giannakoglou KC (2011) Synergetic use of different evaluation, parametrisation and search tools within a hierarchical optimization platform. Appl Soft Comput 11(1):645–651

    Article  Google Scholar 

  • Kapsoulis D, Tsiakas K, Asouti V, Giannakoglou K (2016) The use of Kernel PCA in evolutionary optimization for computationally demanding engineering applications. In: 2016 IEEE symposium series on computational intelligence (SSCI). Athens, Greece, pp 1–8. https://doi.org/10.1109/ssci.2016.7850203

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks

    Google Scholar 

  • Leifsson L, Koziel S, Tesfahunegn Y (2014) “Aerodynamic design optimization: physics-based surrogate approaches for airfoil and wing design”. AIAA SciTech. AIAA 2014-0572

    Google Scholar 

  • Lim D, Jin Y, Ong YS, Sendho B (2010) Generalizing surrogate-assisted evolutionary computation. IEEE Trans Evol Comput 14(3):329–355

    Article  Google Scholar 

  • Marinus BG (Royal Military Academy/VKI for Fluid Dynamics, Belgium), Rogery M (Ecole Centrale de Lyon, France), Braembusschez R (VKI for Fluid Dynamics, Belgium) (2010) Aeroacoustic and aerodynamic optimization of aircraft propeller blades. AIAA

    Google Scholar 

  • Muller SD, Marchetto J, Airaghi S, Koumoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6(1):16–29

    Article  Google Scholar 

  • Peter J, Marcelet M (ONERA, France) (2007) Comparison of surrogate models for turbomachinery design. In: 7th wseas international conference on simulation, modelling and optimization

    Google Scholar 

  • Peter J, Carrier G, Bailly D, Klotz P, Marcelet M, Renac F (March 2011) Local and Global Search Methods for Design in Aeronautics. ONERA J AerospaceLab 2

    Google Scholar 

  • Praveen C, Duvigneau R (2007) Radial basis functions and kriging metamodels for aerodynamic optimization. INRIA Technical report

    Google Scholar 

  • Salcedo-Sanz S, Ortiz-Garcia E, Perez-Bellido A, Portilla A (2011) Short term wind speed prediction based on evolutionary support vector regression algorithms. Expert Syst Appl 38:4052–4057

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359

    Article  MathSciNet  Google Scholar 

  • Szollos A, Smid M, Hájek J (2009) Aerodynamic optimization via multi-objective micro-genetic algorithm with range adaptation: knowledge-based reinitialization, crowding and dominance. Adv Eng Software 40

    Google Scholar 

  • Vankan J, Maas R (2010) Surrogate modeling for efficient design optimization of composite aircraft fuselage panels. In: 27th international congress of the aeronautical sciences, ICAS

    Google Scholar 

  • Won KS, Ray T (2005) A Framework for Design Optimization using Surrogates. Eng Optim 37(7):685–703

    Article  MathSciNet  Google Scholar 

  • Younis A, Jichao G, Zuomin D, Guangyao L (2008) Trends, features, and test of common and recently introduced global optimization methods. In: 2th AIAA/ISSMO multidisciplinary & optimization conference. AIAA 2008-5853

    Google Scholar 

  • Zhong-Hua H, Zimmermann R, Görtz S (DLR, Germany) (2010) A new cokriging method for variable-fidelity surrogate modeling of aerodynamic data. AIAA

    Google Scholar 

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Correspondence to Esther Andrés-Pérez .

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Andrés-Pérez, E. et al. (2019). Garteur AD/AG-52: Surrogate-Based Global Optimization Methods in Preliminary Aerodynamic Design. 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_13

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  • DOI: https://doi.org/10.1007/978-3-319-89890-2_13

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