Adaptive wing aeroelasticity CFD analysis using artificial neural network (ANN)

Abstract

Morphing wing technology has been part of the aviation industry since the time of the Wright Brothers. Wright Brothers used morphing wing for Wright flyer, and type of morphing wing used then was twisting the wing prepared with the help of bicycle tubes and cardboards. But to enhance the morphing wing technology and to produce more effective results aerodynamically, and increase lift performances and lower drag performances, better materials with better mechanism had to be used. This research is directed towards morphing a Supersonic aero elastic wing in Computational Fluid Dynamics (CFD) analysis using Artificial Neural Networks (ANN). Here a CFD analysis of National Advisory Committee for Aeronautics (NACA)0012 airfoil, made of a morphing material is done in the initial part. It has low and transonic regions of CFD analysis in the said airfoil. The problems persisting with the high speed aerodynamic CFD analysis is brought out in detail. Later part of the work is on designing of a self-trainable and multilayer Artificial Deep Neural Network and its programming using Python deep learning (Python Anaconda). It also explains how these Artificial Intelligence (AI) techniques help immensely in supersonic aerodynamic and aeroelastic analysis of the aerofoils. The data from designing the aerofoil and its CFD analysis will be used for training the deep neural networks, which is done by the Python code.