Abstract 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.
Hasan et al. (Wed,) studied this question.