A Novel Framework for Optimizing Gurney Flaps using RBF Neural Network and Cuckoo Search Algorithm
Aryan Tyagi, Paras Singh, Aryaman Rao, Gaurav Kumar, Raj Kumar Singh

TL;DR
This paper introduces a new optimization framework combining RBF neural networks and Cuckoo Search to improve Gurney flap design parameters, significantly enhancing aerodynamic performance in aircraft and wind turbines.
Contribution
It presents a novel integration of RBF surrogate modeling with Cuckoo Search for Gurney flap optimization, demonstrating superior results over other algorithms.
Findings
10.28% improvement in Cl/Cd ratio
Optimal flap height of 1.9%c and angle of -58 degrees
Effective framework for aerodynamic optimization
Abstract
Enhancing aerodynamic efficiency is vital for optimizing aircraft performance and operational effectiveness. It enables greater speeds and reduced fuel consumption, leading to lower operating costs. Hence, the implementation of Gurney flaps represents a promising avenue for improving airfoil aerodynamics. The optimization of Gurney flaps holds considerable ramifications for improving the lift and stall characteristics of airfoils in aircraft and wind turbine blade designs. The efficacy of implementing Gurney flaps hinges significantly on its design parameters, namely, flap height and mounting angle. This study attempts to optimize these parameters using a design optimization framework, which incorporates training a Radial Basis Function surrogate model based on CFD data from two-dimensional (2D) Reynolds-Averaged Navier-Stokes (RANS) simulations. The Cuckoo Search algorithm is then…
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Taxonomy
TopicsWind Energy Research and Development
