A Neural Network Investigation for an Aerodynamic Prediction

Abstract

In the present thesis a Machine Learning approach in Fluid Mechanics field was investigated. In particular Artificial Neural Networks were used to predict lift and drag coefficient of NACA 4 digit airfoils. In the last years the application of Artificial Intelligence and in particular of Machine Learning to scientific disciplines increased substantially. Machine Learning offers techniques to extract information and knowledge from data and it provides the possibility to handle with massive quantitative of data. The ojective of the related work was to investigate how Machine Learning is working, in particular Neural Networks, and how it has to be applied in order to make a prediction. The preliminary phase of the work was to create the data-set necessary for the secondary phase, the Neural Network analysis. The generation of the dataset involved CFD simulations. Those were performed with DLR-TAU code, a finite volume method for RANS equations. A tool as a code for RANS equations were used because of its ability to capture the aerodynamic coefficients of interest in the related work. The preliminary phase includes also all the steps that a CFD simulation concerns: CAD generation performed with Geocreate, mesh generation with Centaur and numerical simulation with DLR-TAU code. The related theoretical background is given in chapter 1, instead in chapter 3 numerical simulations are presented. In the context of neural network approach the software package Google Tensorflow 2 via Python3 interface was used. Therefore, in the context of this thesis, artificial neural networks were used to manage lift and drag coefficient generated from CFD simulations. This work presents in chapter 2 an overview of what is Machine Learning and a detailed introduction to artificial neural networks. Final results and considerations are shown in chapter 4.