Airfoil aerodynamic design represents an essential domain in aircraft development, where the pursuit of advanced and intelligent optimization strategies is important for achieving significant advancements. In this paper, we demonstrate the effectiveness and versatility of reinforcement learning (RL)-based optimization methods in enhancing aerodynamic performance for both transonic and supersonic airfoils. We introduced a novel methodology using RL to optimize airfoil designs, leveraging ADflow as the aerodynamic solver and constructing an RL environment where Class-Shape Transformation (CST) parameters describe the airfoil geometry, transforming it into a finite state variable. Key flow field features, especially shock waves, were incorporated to guide the optimization process, enabling the RL model to iteratively improve designs based on real-time feedback from simulations. Applied to transonic airfoils, this method yielded remarkable results, including a 70.20% increase in the lift-to-drag ratio for one airfoil, with consistent improvements across various initial geometries and flight conditions. Extending to the NASA SC(2)-0404 supersonic airfoil, the optimized design achieved significant geometric changes that resulted in a 6.25% increase in the lift-to-drag ratio, with improvements ranging from 4.90% to 25.46% across different lift coefficients. These findings highlight the robustness and adaptability of RL techniques in addressing the unique challenges of both transonic and supersonic aerodynamics while maintaining structural integrity.
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