Research Information System
FLOW TURBULENCE AND COMBUSTION, vol.116, no.3, 2026 (SCI-Expanded, Scopus)
Conventional airfoils suffer from aerodynamic limitations, including early stall onset and increased drag at higher angles of attack, which restrict their operational efficiency and versatility. Active flow control methods, such as the Co Flow Jet (CFJ), have shown promise in overcoming these challenges. This study investigated whether a Dual Co Flow Jet (DCFJ) system with independently controlled suction and blowing slots may extend the stable working range of the airfoil. Building on these foundations, airfoils were tested at various free stream velocities and angles of attack, ranging from 0 degrees to 24 degrees, each corresponding to multiple coefficients of jet momentum. Compared to the baseline airfoil, the DCFJ configuration not only greatly improves lift-to-drag ratios but also raises maximum lift coefficients and delays stall. To further reduce the cost and complexity of full-scale testing, numerous machine learning (ML) models, including multi-layer artificial neural networks, were trained on experimental datasets. These models achieved minimal prediction errors for lift and drag coefficients, providing a reliable and computationally efficient alternative to conventional experimental workflows.