Data Mining and Machine Learning Techniques for Aerodynamic Databases: Introduction, Methodology and Potential Benefits

Esther Andrés-Pérez

doi:10.3390/en13215807

Abstract

Machine learning and data mining techniques are nowadays being used in many business sectors to exploit the data in order to detect trends, discover certain features and patters, or even predict the future. However, in the field of aerodynamics, the application of these techniques is still in the initial stages. This paper focuses on exploring the benefits that machine learning and data mining techniques can offer to aerodynamicists in order to extract knowledge from the CFD data and to make quick predictions of aerodynamic coefficients. For this purpose, three aerodynamic databases (NACA0012 airfoil, RAE2822 airfoil and 3D DPW wing) have been used and results show that machine-learning and data-mining techniques have a huge potential also in this field.

Highlights

In the field of aerodynamics, complex steady flows are simulated by computational fluid dynamics (CFD) daily in the industry since CFD tools have already reached an acceptable level of maturity
Machine learning techniques commonly used in the area of artificial intelligence (AI) and data mining (DM) can represent a valuable support to reduce the computational cost required for aerodynamic analysis
Each ofItthe steps in thetofigure above willallbethe explained applied tointhe selected aerodynamic databases

Summary

Introduction

In the field of aerodynamics, complex steady flows are simulated by computational fluid dynamics (CFD) daily in the industry since CFD tools have already reached an acceptable level of maturity. These simulations are usually performed over full-aircraft configurations or several aircraft components where meshes of hundreds of million points are required in order to provide precise features of the flow. Simulations are performed for different parameters to properly explore the design space This implies a high computational cost that may be, in certain situations, even infeasible nowadays. Machine learning techniques commonly used in the area of artificial intelligence (AI) and data mining (DM) can represent a valuable support to reduce the computational cost required for aerodynamic analysis

Objectives

Results

Conclusion