Surrogate Unsteady Aerodynamic Modeling with Autoencoders and Long-Short Term Memory Networks

Hüseyin Emre Tekaslan, Yusuf Demiroğlu, Melike Nikbay

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

This paper presents preliminary results of an ongoing research in prediction of time-dependent flow fields by focusing on data-driven surrogate modeling using artificial neural networks for unsteady aerodynamic problems. The aim of this research is to model unsteady flow fields with learning in low-dimensional space and reconstruct with recurrent autoencoders. Within the scope of this paper, we separately share our findings in viscous unsteady flow field reconstruction of a 2D cylinder in a channel with a deep autoencoder and unsteady aerodynamic-acoustic time-series prediction of the supersonic NASA C25D aircraft with shallow long short-term memory networks. Satisfactory results are achieved in both unsteady applications, yet further improvements and validations are needed to be achieved to establish the desired surrogate unsteady aerodynamic modeling for supersonic aircraft maneuvers.

Original languageEnglish
Title of host publicationAIAA SciTech Forum 2022
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624106316
DOIs
Publication statusPublished - 2022
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 - San Diego, United States
Duration: 3 Jan 20227 Jan 2022

Publication series

NameAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
Country/TerritoryUnited States
CitySan Diego
Period3/01/227/01/22

Bibliographical note

Publisher Copyright:
© 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

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