Predicting airfoil separation bubble locations using ABCP algorithms

Abstract

In this study, new equations to predict the location parameters of the laminar separation bubble that occur in the flow over the blade/wing and negatively affect the blade/wing aerodynamic performance in unmanned aerial vehicles and wind turbines were developed first in the literature by Artificial Bee Colony Programming (ABCP) and quick ABCP (qABCP) algorithms. Data from the experimental study for NACA2415 were processed using ABCP and qABCP methods. The results of the models were also compared with the results of the XFOIL code, a numerical analysis in the literature, and an Artificial Neural Network (ANN). Even though low Reynolds numbers with more viscous effects were not given in the training data, both ABCP and qABCP algorithms successfully estimated the separation (Xs) and the reattachment points (Xr). Considering the error analysis and correlation coefficient values, it was seen that both algorithms can be used for both Xs and Xr predictions. Users/designers of the aerospace and energy industry can use to estimate Xr and Xs points for the NACA 2415 airfoil using the new equations proposed in this study at Re numbers ranging from 50,000 to 300,000, without the need for expensive and time-consuming experiments or Computational Fluid Dynamics (CFD) analysis. Furthermore, it was concluded that ABCP methods not only have the advantage of flexibly building models but are also highly competitive with other machine learning methods used in the literature for prediction, such as ANN.