Abstract
A significant challenge to the application of evolutionary multiobjective optimization (GlossaryTerm
EMO
) for transonic airfoil design is the often excessive number of computational fluid dynamic (GlossaryTermCFD
) simulations required to ensure convergence. In this study, a multiobjective particle swarm optimization (GlossaryTermMOPSO
) framework is introduced, which incorporates designer preferences to provide further guidance in the search. A reference point is projected onto the Pareto landscape by the designer to guide the swarm towards solutions of interest. The framework is applied to a typical transonic airfoil design scenario for robust aerodynamic performance. Time-adaptive Kriging models are constructed based on a high-fidelity Reynolds-averaged Navier–Stokes (GlossaryTermRANS
)solver to assess the performance of the solutions. The successful integration of these design tools is facilitated through the reference point, which ensures that the swarm does not deviate from the preferred search trajectory. A comprehensive discussion on the proposed optimization framework is provided, highlighting its viability for the intended designAccess this chapter
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Abbreviations
- CFD:
-
computational fluid dynamics
- EA:
-
evolutionary algorithm
- EMO:
-
evolutionary multiobjective optimization
- FMG:
-
full multi-grid
- LHS:
-
latin hypercube sampling
- MOO:
-
multi-objective optimization
- MOP:
-
multiobjective problem
- MOPSO:
-
multiobjective particle swarm optimization
- NASA:
-
National Aeronautics and Space Administration
- NSGA:
-
nondominated sorting genetic algorithm
- NSPSO:
-
nondominated sorting particle swarm optimization
- PSO:
-
particle swarm optimization
- RANS:
-
Reynolds-averaged Navier–Stokes
- SOM:
-
self-organizing map
- UPMOPSO:
-
user-preference multiobjective PSO
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Carrese, R., Li, X. (2015). Preference-Based Multiobjective Particle Swarm Optimization for Airfoil Design. 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_67
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