Deep-Learning-Based Inverse Airfoil Design Using Global Aerodynamic Performance Metrics

This paper introduces a framework for airfoil inverse design, using a deep-learning approach and incorporating both the parametric section (PARSEC) and class–shape transformation (CST) airfoil parameterization techniques for comparative evaluation. A predictive model has been established to estimate the PARSEC and CST parameters that characterize airfoil shapes based on a range of aerodynamic coefficients as inputs. This model is constructed through the training of a neural network using an aerodynamic dataset generated from XFOIL simulations. The developed model demonstrates its capacity to predict airfoil geometries with reasonable accuracy, effectively linking aerodynamic coefficients with corresponding shapes. A multihead self-attention block is incorporated as a feature-interaction module to process correlated aerodynamic input descriptors before mapping them to geometry parameters. The findings, derived from both the PARSEC and CST parameterization techniques, indicate that the framework reconstructs airfoil geometries with good agreement across diverse airfoil families within the considered Reynolds number and angle-of-attack envelope.