An airfoil inverse design method based on target testing conditional generative adversarial network

A Target Testing Conditional Generative Adversarial Network (TT-CGAN) is developed and applied to airfoil inverse design. This network extends the Conditional Generative Adversarial Network (CGAN) by integrating a Multi-Layer Perceptron (MLP) tester, so as to enhance the capability of CGAN in evaluating the impact of additional conditions on target testing. Utilizing the UIUC airfoil database, 797 real airfoils were selected, and their corresponding aerodynamic parameters were calculated by solving the Reynolds-Averaged Navier-Stokes (RANS) equations to construct a comprehensive airfoil database. The airfoil shapes were parameterized using the Class Shape Transformation (CST) method, transforming the geometric parameters from 100 to 14 CST parameters. Multi-modal aerodynamic parameters, including lift coefficient, drag coefficient, and surface pressure distribution, were fused using the feature-level fusion approach. These parameters were compared with the aerodynamic parameters based solely on lift and drag coefficients, which served as auxiliary conditions for the network during the airfoil inverse design process. The results indicate that the TT-CGAN based inverse design method generates more accurate airfoils, with an average root mean square error of 1.779×10^-3 and an average mean absolute error of 1.351×10^-3 in airfoil geometry. The generated airfoils were further validated through numerical simulations by solving the RANS equations, demonstrating an average relative error of 3.599 8% for the lift coefficient and 3.723 9% for the drag coefficient, confirming that the generated airfoils can meet the specified design criteria. Analysis of the lift-to-drag ratio distributions reveals that 40% of the test airfoils achieved lift-to-drag ratios within the [20, 30) range, compared to only 16% in the training set. This finding highlights the method's capability to make accurate predictions even within data-sparse regions, showcasing its generalizability.