Physics-Informed Machine Learning Framework for Rapid Aerodynamic Prediction of Trailing-Edge Morphing Airfoils at Low Reynolds Numbers
Abstract
Abstract Trailing-edge morphing airfoils with parabolic camber profiles offer superior aerodynamic performance over conventional hinged surfaces, yet Reynolds-Averaged Navier–Stokes (RANS) simulation cost constrains design-space exploration to sparse grids. This study validates a physics-informed machine learning (PIML) surrogate framework trained on 456 ANSYS Fluent cases spanning two morphing architectures — Parabolic Flap (PF) and Traditional Flap (TF) — across three TEMA configurations (50:50, 60:40, 70:30 chord ratios), a NACA 2412 baseline, eight angles of attack (− 3° to + 18°), four deflection angles (0°–15°), and three Reynolds numbers (1.91×10⁵–3.18×10⁵). Four surrogate architectures — Random Forest, Gradient Boosting (GB), Multilayer Perceptron, and Gaussian Process Regression (GPR) — simultaneously predict lift, drag, pitching moment, lift-to-drag ratio, and five surface pressure metrics. Physics-informed feature engineering incorporating trigonometric angle-of-attack decomposition and a dimensionless δ²/Re interaction term reduces prediction error by up to 18%. The GB surrogate achieves cl R² = 0.974 (5-fold CV: 0.974 ± 0.007), while GPR provides calibrated 95% confidence intervals identifying post-stall regimes as maximum-uncertainty zones. Design-space maps at zero CFD cost confirm PF universally outperforms TF; PF TEMA 50:50 achieves l/d = 184.6 versus 124.2 for TF at Re = 3.18×10⁵, with surrogate evaluation completing in under two seconds.
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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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