Digital Twin-Oriented Physics-Informed Neural Networks for Airfoil Aerodynamics Analysis
Aerodynamic analysis, a core aspect of studying physical phenomena, relies on governing equations traditionally solved via computational fluid dynamics (CFD). While CFD enables high-fidelity simulations of complex flows, including the Navier–Stokes equations, it often requires simplifying assumptions and substantial computational resources, limiting real-time analysis. Physics-Informed Neural Networks (PINNs) offer a promising alternative by embedding physical laws into the training process, ensuring predictions remain consistent with aerodynamic principles. This study presents a systematic implementation and validation of PINNs for predicting lift (Cl) and drag (Cd) coefficients across NACA airfoil geometries under varying Reynolds numbers and angles of attack. The methodology leverages CFD-generated data for training and incorporates physics-based loss functions enforcing linear lift theory, symmetry, positive drag, and drag–lift relationships. The trained PINN is integrated into an interactive Digital Twin environment, enabling real-time aerodynamic analysis, design exploration, and optimization. Results demonstrate rapid convergence, with total loss decreasing four orders of magnitude and mean absolute errors of 0.049 for Cl and 0.0019 for Cd, corresponding to 2.4% and 12.5% relative errors, respectively. Lift predictions show excellent agreement with CFD (R² = 0.993), while drag predictions capture trends with moderate scatter (R² = 0.738). Case studies of symmetric, low-camber, and high-camber airfoils confirm accurate reproduction of expected aerodynamic behavior. This work highlights the feasibility of combining CFD, physics-informed AI, and Digital Twin technologies to accelerate and enhance aerodynamic workflows.