Predicting surface pressure fields and lift coefficients of subsonic airfoils by machine-learning-enhanced compressive sensing framework

Aerodynamic performance assessment is pivotal in aircraft design optimization, yet traditional airfoil optimization methods remain computationally expensive and inefficient. To address this challenge, we introduce a machine-learning-enhanced compressive sensing (ML-CS) framework that synergizes data-driven learning with compressive sensing to accurately predict surface pressure fields and lift coefficients of subsonic airfoils. A geometry-informed sparse sampling strategy is developed using airfoil camber-thickness curve parameterization, enabling efficient feature extraction from limited training data. The development of the ML-CS framework employs a three-stage process: first, the ML-CS reconstruction efficiency is optimized, achieving more than 92% accuracy in pressure coefficient prediction using only ten sparse sensors; second, a Kriging-surrogate-assisted sensor value prediction method that robustly preserves prediction accuracy despite missing sensor data is proposed; third, a lift coefficient prediction module that reduces errors by 35.5% compared to conventional surrogate models while simultaneously reconstructing surface pressure distributions is proposed. The framework demonstrates superior efficiency and generalizability, offering a paradigm shift in aerodynamic surrogate modeling.