Aero-structural characterization of wing planforms using machine learning

This study introduces a machine learning (ML) framework for efficient aero-structural characterization of wing planform shapes, addressing the computational challenges posed by traditional CFD and FEA methods. The goal is to develop an ML-based aero-structural optimization framework that replaces time intensive computational tasks with faster yet reliable approach. Leveraging advanced parameterization, data normalization, and reduced-order modeling (ROM), two regressor chain-based surrogate models are developed to predict aerodynamic and structural responses from identical shape parameters. For CFD buffet predictions, a Proper Orthogonal Decomposition-based surrogate model (POD-ML) is employed, while for stress predictions, the data is first clustered, and then POD is applied within each cluster, forming the K-means ML model. Using the ONERA M6 wing as a baseline, the surrogate models' performance is evaluated across various shape parameters like sweep, dihedral, and twists. The models effectively predict aerodynamic responses, such as pressure coefficients (C_p), with a maximum Mean Squared Error (MSE) of 0.05, requiring only 1,000 training samples — significantly fewer than conventional neural network models. The models also handle complex structural predictions with good accuracy, particularly for stress fields, achieving a maximum MSE of 0.10. Comparisons reveal that K-means ML outperforms POD-ML in stress field prediction, though it performs slightly worse for aerodynamic responses. Together, these methods provide substantial computational savings, facilitating rapid design iterations and advancing the optimization of wing architectures.