A deep neural network reduced order model for unsteady aerodynamics of pitching airfoils-Reference-Cited by-同舟云学术

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A deep neural network reduced order model for unsteady aerodynamics of pitching airfoils

Published:2024-09 Issue: Volume:152 Page:109345
ISSN:1270-9638
Container-title:Aerospace Science and Technology
language:en
Short-container-title:Aerospace Science and Technology

Author:

Baldan Giacomo^ORCID,Guardone Alberto^ORCID

Publisher

Elsevier BV

Reference37 articles.

1. Review of rotating wing dynamic stall: experiments and flow control;Gardner;Progress in Aerospace Sciences,2023

2. On computational simulations of dynamic stall and its three-dimensional nature;Khalifa;Phys. Fluids,2023

3. Effect of convective schemes in wall-resolved and wall-modeled LES of compressible wall turbulence;De Vanna;Comput. Fluids,2023

4. On the coupling between wall-modeled LES and immersed boundary method towards applicative compressible flow simulations;De Vanna;Comput. Fluids,2023

5. Influence of free-stream turbulence intensity on static and dynamic stall of a NACA 0018 aerofoil;Damiola;J. Wind Eng. Ind. Aerodyn.,2023

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