Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural Network

• Published in: arXiv.org 2021-09
• Main Authors: Moin, Hassan, Hafiz Zeeshan Iqbal Khan, Surrayya Mobeen, Riaz, Jamshed
• Format: Article
• Language: English
• Subjects: Aerodynamic characteristics; Aerodynamic coefficients; Aerodynamics; Aeroelastic stability; Airfoils; Angle of attack; Artificial neural networks; Computational fluid dynamics; Computer architecture; Dynamic stability; Equilibrium flow; Flight control systems; Fluid dynamics; Fluid flow; Incompressible flow; Mathematical models; Neural networks; Reynolds number; Rotary wing aircraft; Wind tunnel testing
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2109.12149

Figuring out the right airfoil is a crucial step in the preliminary stage of any aerial vehicle design, as its shape directly affects the overall aerodynamic characteristics of the aircraft or rotorcraft. Besides being a measure of performance, the aerodynamic coefficients are used to design additional subsystems such as a flight control system, or predict complex dynamic phenomena such as aeroelastic instability. The coefficients in question can either be obtained experimentally through wind tunnel testing or, depending upon the accuracy requirements, by numerically simulating the underlying fundamental equations of fluid dynamics. In this paper, the feasibility of applying Artificial Neural Networks (ANNs) to estimate the aerodynamic coefficients of differing airfoil geometries at varying Angle of Attack, Mach and Reynolds number is investigated. The ANNs are computational entities that have the ability to learn highly nonlinear spatial and temporal patterns. Therefore, they are increasingly being used to approximate complex real-world phenomenon. However, despite their significant breakthrough in the past few years, ANNs' spreading in the field of Computational Fluid Dynamics (CFD) is fairly recent, and many applications within this field remain unexplored. This study thus compares different network architectures and training datasets in an attempt to gain insight as to how the network perceives the given airfoil geometries, while producing an acceptable neuronal model for faster and easier prediction of lift, drag and moment coefficients in steady state, incompressible flow regimes. This data-driven method produces sufficiently accurate results, with the added benefit of saving high computational and experimental costs.