Główne pojęcia
The author presents a mechanism-informed reinforcement learning framework for airfoil shape optimization, addressing complexities in optimizing shapes governed by fluid dynamics. The approach integrates Laplacian smoothing, adaptive refinement, and Bézier curves for efficient dimensionality reduction.
Streszczenie
The study introduces a novel reinforcement learning framework for airfoil shape optimization. Leveraging the twin delayed deep deterministic policy gradient algorithm, the approach addresses challenges in optimizing shapes influenced by fluid dynamics. By integrating Laplacian smoothing, adaptive refinement, and Bézier curves, the method ensures precise manipulation of airfoil geometry while reducing mesh tangling issues. The neural network architecture enhances learning efficiency and geometric accuracy through dimensionality reduction.
Key points:
- Introduction of a reinforcement learning framework for airfoil shape optimization.
- Utilization of the twin delayed deep deterministic policy gradient algorithm.
- Integration of Laplacian smoothing, adaptive refinement, and Bézier curves.
- Focus on precise manipulation of airfoil geometry and reduction of mesh tangling issues.
- Enhancement of learning efficiency and geometric accuracy through dimensionality reduction.
Statystyki
"Dual-weighted residual-based mesh refinement strategy is applied to ensure accurate calculation of target functionals."
"Neural network architecture leverages Bézier curves for efficient dimensionality reduction."