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Aerodynamic Design with Physics-Based Surrogates

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Springer Handbook of Computational Intelligence

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Abstract

Details, references and guidelines are given about the adoption of surrogate models and reduced-order models within the aerodynamic shape optimization context. The aerodynamic design problem and its approximated version are introduced and discussed and then, an overview of various surrogate models and surrogate-based optimization methods is given. Subsequently, the concept of model order reduction is recalled, and the performance analysis of reduced-order models based on proper orthogonal decomposition (GlossaryTerm

POD

) is discussed. Within this context, some techniques to adaptively and globally improve the accuracy of GlossaryTerm

POD

-based surrogates are illustrated. Finally, an aerodynamic shape design problem of a transonic airfoil is used to practically analyze and compare the performances of various surrogate-based optimization methods.

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Abbreviations

ADGLIB:

adaptive genetic algorithm optimization library

AFPGA:

adaptive full POD genetic algorithm

AMPGA:

adaptive mixed-flow POD genetic algorithm

CFD:

computational fluid dynamics

CS:

cell saving

CST:

class-shape transformation

DACE:

design and analysis of computer experiments

DGA:

direct genetic algorithm

DOE:

design of experiment

EGO:

efficient global optimization

EI:

expected improvement

FOM:

full-order model

FPGA:

full POD genetic algorithm

GA:

genetic algorithm

JEGA:

John Eddy genetic algorithm

KGA:

Kriging-driven genetic algorithm

LHS:

latin hypercube sampling

MPE:

mean percentage error

MPGA:

mixed-flow POD genetic algorithm

PCA:

principal component analysis

POD:

proper orthogonal decomposition

RANS:

Reynolds-averaged Navier–Stokes

RBF:

radial basis function

ROM:

reduced-order model

SBO:

surrogate-based optimization

SBSO:

surrogate based shape optimization

SDPE:

standard deviation percentage error

SM:

surrogate model

SOGA:

single-objective genetic algorithm

SVD:

singular value decomposition

TS:

time saving

ZEN:

Zonal Euler–Navier–Stokes

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Authors and Affiliations

  1. Fluid Dynamics Lab., CIRA, Italian Aerospace Research Center, Via Maiorise, 81043, Capua (CE), Italy

    Emiliano Iuliano & Domenico Quagliarella

Authors
  1. Emiliano Iuliano
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  2. Domenico Quagliarella
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Correspondence to Emiliano Iuliano .

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Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Iuliano, E., Quagliarella, D. (2015). Aerodynamic Design with Physics-Based Surrogates. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_60

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

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