Toward the Detection of Flow Separation for Operating Airfoils Using Machine Learning

Turbulent flow separation over lifting surfaces impacts high-lift systems such as aircraft, wind turbines, and turbomachinery, and contributes to noise, lift loss, and vibrations. Accurate detection of flow separation is therefore essential to enable active control strategies and to mitigate its adverse effects. Several machine learning models are compared for detecting flow separation from surface pressure fluctuations. The models were trained on experimental data covering various airfoils, angles of attack (0°–23°), and Reynolds numbers, with Rec=0.8–4.5×106. For supervised learning, the ground-truth binary labels (attached or separated flow) were derived from static pressure distributions, lift coefficients, and the power spectral densities of surface pressure fluctuations. Three machine learning techniques (multilayer perceptron, support vector machine, logistic regression) were utilized with fine-tuned hyperparameters. Promising results are obtained, with the support vector machine achieving the highest performance (accuracy 0.985, Matthews correlation coefficient 0.975), comparable to other models, with advantages in runtime and model size. However, most misclassifications occur near separation onset due to gradual transition, suggesting areas for model refinement. Sensitivity to database parameters is discussed alongside flow physics and data quality.