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Enhancing aerodynamic efficiency is vital for optimizing aircraft performanceand operational effectiveness. It enables greater speeds and reduced fuelconsumption, leading to lower operating costs. Hence, the implementation ofGurney flaps represents a promising avenue for improving airfoil aerodynamics.The optimization of Gurney flaps holds considerable ramifications for improvingthe lift and stall characteristics of airfoils in aircraft and wind turbineblade designs. The efficacy of implementing Gurney flaps hinges significantlyon its design parameters, namely, flap height and mounting angle. This studyattempts to optimize these parameters using a design optimization framework,which incorporates training a Radial Basis Function surrogate model based onCFD data from two-dimensional (2D) Reynolds-Averaged Navier-Stokes (RANS)simulations. The Cuckoo Search algorithm is then employed to obtain the optimaldesign parameters and compared with other competing optimization algorithms.The optimized Gurney flap configuration shows a notable improvement of 10.28%in Cl/Cd, with a flap height of 1.9%c and a flap mounting angle of -58 degrees.The study highlights the effectiveness of the proposed design optimizationframework and furnishes valuable insights into optimizing Gurney flapparameters. The comparison of metaheuristic algorithms serves to enhance thestudy's contribution to Gurney flap design optimization.

A Novel Framework for Optimizing Gurney Flaps using RBF Neural Network and Cuckoo Search Algorithm