  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.
