Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality

Abstract

Effective airfoil geometry optimization requires exploring a diverse range of
designs using as few design variables as possible. This study introduces AirDbM, a
Design-by-Morphing (DbM) approach specialized for airfoil optimization that systematically reduces design-space dimensionality. AirDbM selects an optimal set
of 12 baseline airfoils from the UIUC airfoil database, which contains over 1,600
shapes, by sequentially adding the baseline that most increases the design capacity.
With these baselines, AirDbM reconstructs 99% of the database with a mean
absolute error below 0.005, which matches the performance of a previous DbM
approach that used more baselines. In multi-objective aerodynamic optimization,
AirDbM demonstrates rapid convergence and achieves a Pareto front with a greater
hypervolume than that of the previous larger-baseline study, where new Pareto optimal solutions are discovered with enhanced lift-to-drag ratios at moderate stall tolerances. Furthermore, AirDbM demonstrates outstanding adaptability for reinforcement learning (RL) agents in generating airfoil geometry when compared to
conventional airfoil parameterization methods, implying the broader potential of
DbM in machine learning-driven design.

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Identifiers

ORCID for Haris Moazam Sheikh: orcid.org/0000-0002-3154-0494

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