Prediction of airfoil dynamic stall response using convolutional neural networks

Author(s)

Miotto, Renato ; Wolft, William

• Document type: Journal article
• Source: AIAA AVIATION 2023 FORUM; v. N/A, p. 10-pg., 2023-01-01.

Abstract

Convolutional neural network (CNN) models are developed to predict the aerodynamic response from images of the flowfield of an airfoil under dynamic stall. Here, we take the aerodynamic coefficients and pressure distribution as examples. The networks are capable of identifying relevant flow features present in the images and associate them to the airfoil response, while effectively interpolating and extrapolating between flow parameters. This suggests that flow imaging may offer a promising alternative for sensors in experimental campaigns and for building robust surrogate models of complex unsteady flows. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 22/09196-4 - Improving the understanding of unsteady aerodynamic flows via high-fidelity simulations, analytical modeling and deep learning techniques
• Grantee: Renato Fuzaro Miotto
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 21/06448-0 - High-fidelity numerical simulations applied in unsteady aerodynamics, turbulence and aeroacoustics
• Grantee: William Roberto Wolf
• Support Opportunities: Research Grants - Young Investigators Grants - Phase 2