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 GlossaryTermPOD
-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.Access this chapter
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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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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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