Paper Title
Exploring The Use of Ml Algorithms in Active Flow Control Strategy to Facilitate Real-Time Prediction of Aerodynamic Forces of Multi-Element Wings

The task of determining the aerodynamic properties of a multi-element airfoil has historically been a difficult, time-consuming process, and impractical for real-time applications. Machine learning (ML) and deep learning (DL) have shown promise in predicting aerodynamic properties. However, there has been limited research on using these techniques for multi-wing systems. In this study, we explore the use of ML algorithms to predict the aerodynamic performance of a multielement wing, which has not been attempted in the literature. Training them on data generated using ANSYS Fluent, the models can predict these aerodynamic forces based on input parameters. Our approach has potential applications in optimizing the aerodynamic performance of high-speed vehicles, and may inspire further research in this area. The use of ensembling techniques further reduces computation time and power during offline stages. Thus reducing time taken for a cfd analysis from 10-15 minutes to 10-60 microseconds making it 1.0-6.0 ∗ 108 times faster. Keywords - Airfoil, Rear Wing, Deep Learning, Multi-Layer Perceptron, Autoencoders