用12个基型高效优化机翼，性能媲美更复杂方法。
Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality
- 从1600个机翼中选出12个最优基型，降低设计空间维度。
- 重建99%数据库，误差低于0.005，性能与更大基集相当。
- 适合强化学习生成机翼，可发现更高升阻比新解。
高效的机翼几何优化需在最少设计变量下探索多样化设计。本文提出面向机翼优化的DbM方法AirDbM，系统性降低设计空间维度。通过逐次选择最能提升设计容量的基型，从包含1600种形状的UIUC机翼数据库中筛选出12个最优基型。利用这些基型，AirDbM可重构99%的数据库，平均绝对误差低于0.005，性能与此前使用更多基型的DbM方法相当。在多目标气动优化中，AirDbM实现快速收敛，获得的帕累托前沿具有更大超体积，且在中等失速容忍度下发现了具有更高升阻比的新帕累托最优解。此外，相较于传统参数化方法，AirDbM在强化学习代理生成机翼几何时展现出卓越适应性，表明DbM在机器学习驱动设计中的广泛潜力。
原文摘要 · Abstract (English)
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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