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

arXiv 2510.16020 ↗First posted 15 Oct 2025cs.LG physics.flu-dyncited by 0

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Abstract (arXiv)

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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References · 10

  1. 2020pymoo: Multi-objective Optimization in Pythoncited by 200
  2. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  3. 2020Airfoil Design Parameterization and Optimization using B'ezier Generative Adversarial Networkscited by 35
  4. 2020The Hypervolume Indicator: Problems and Algorithmscited by 12
  5. 2022Parametric Generative Schemes with Geometric Constraints for Encoding and Synthesizing Airfoilscited by 6
  6. 2022Optimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimizationcited by 3
  7. 2024Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Modelscited by 3
  8. 2024A Survey on Design-space Dimensionality Reduction Methods for Shape Optimizationcited by 3
  9. 2024A systematic dataset generation technique applied to data-driven automotive aerodynamicscited by 3
  10. 2022Airfoil Optimization using Design-by-Morphingcited by 1

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  3. 2025Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
  4. 2025Adjoint-Based Aerodynamic Shape Optimization with a Manifold Constraint Learned by Diffusion Models
  5. 2024Airfoil Diffusion: Denoising Diffusion Model For Conditional Airfoil Generation
  6. 2024A mechanism-driven reinforcement learning framework for shape optimization of airfoils
  7. 2026Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design
  8. 2025BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft