A divide-and-conquer machine learning transition model for airfoil flows: From steady linear-lift to unsteady stall stage

Key Points

• The model effectively predicts transitions in airfoil flows, enhancing understanding of lift behavior.
• Key metric includes identifying critical flow stages such as steady linear-lift and unsteady stall.
• The approach involves a divide-and-conquer machine learning model to analyze complex airfoil dynamics.
• This model shows potential for advancing design efficiency in aerodynamic applications.