The present study introduces a Gauss-Seidel fluid-structure interaction (FSI) method including the flow solver, structural statics solver and a fast data transfer technique, for the research of structural deformation and flow field variation of rotor blades under the combined influence of steady aerodynamic and centrifugal forces. The FSI method is illustrated and validated by the static aeroelasticity analysis of a transonic compressor rotor blade, NASA Rotor 37. An improved local interpolation with data reduction (LIWDR) algorithm is introduced for fast data transfer on the fluid-solid interface of blade. The results of FSI calculation of NASA Rotor 37 show that when compared with the radial basis function (RBF) based interpolation algorithm, LIWDR meets the interpolation accuracy requirements, while the calculation cost can be greatly improved. The data transmission time is only about 1% of that of RBF. Moreover, the iteration step of steady flow computation within one single FSI has little impact on the converged aerodynamic and structural results. The aerodynamic load-caused deformation accounts for nearly 50% of the total. The effects of blade deformation on the variations of aerodynamic performance are given, demonstrating that when static aeroelasticity is taken into account, the choke mass flow rate increases and the peak adiabatic efficiency slightly decreases. The impact mechanisms on performance variations are presented in detail.

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.