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

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.

[1]  M. Fossati.  Evaluation of aerodynamic loads via reduced order methodology , 2015 .

[2]  Zhenghong Gao,et al.  Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation , 2020, Chinese Journal of Aeronautics.

[3]  Daniel Viúdez-Moreiras,et al.  PERFORMANCE COMPARISON OF KRIGING AND SVR SURROGATE MODELS APPLIED TO THE OBJECTIVE FUNCTION PREDICTION WITHIN AERODYNAMIC SHAPE OPTIMIZATION , 2016 .

[4]  Zilong Ti,et al.  Wake modeling of wind turbines using machine learning , 2020 .

[5]  Richard D. Sandberg,et al.  Turbulence Model Development using CFD-Driven Machine Learning , 2019 .

[6]  Xiangyang Wang,et al.  Aerodynamic shape optimization using a novel optimizer based on machine learning techniques , 2019, Aerospace Science and Technology.

[7]  Bento Silva de Mattos,et al.  Artificial neural networks to predict aerodynamic coefficients of transport airplanes , 2017 .

[8]  Qing Wang,et al.  Unsteady aerodynamic modeling at high angles of attack using support vector machines , 2015 .

[9]  Pradip Dube,et al.  Machine Learning Approach to Predict Aerodynamic Performance of Underhood and Underbody Drag Enablers , 2020 .

[10]  Weiwei Zhang,et al.  Multi-kernel neural networks for nonlinear unsteady aerodynamic reduced-order modeling , 2017 .

[11]  Robert Youngblood,et al.  Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD) , 2020 .

[12]  Ayman Moawad,et al.  Vehicle energy consumption estimation using large scale simulations and machine learning methods , 2019, Transportation Research Part C: Emerging Technologies.

[13]  Djamel Lakehal,et al.  Towards an integrated machine-learning framework for model evaluation and uncertainty quantification , 2019 .

[14]  Linyang Zhu,et al.  Machine learning methods for turbulence modeling in subsonic flows around airfoils , 2018, Physics of Fluids.