Recurrent-Neural Network Prediction of Lift on an Oscillating Plate

Nida Ahsan, Mahmoud Ayyad, Muhammad R. Hajj, Imran Akhtar

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

2 Scopus citations

Abstract

We assess the capability of recurrent neural networks in predicting unsteady forces on moving structures in fluids. We consider the case of a plunging oscillating flat plate and aim at predicting the unsteady lift force using relatively short-time of few cases having different excitation amplitudes. The approach is based on predicting temporal coefficients of modes generated by applying proper-orthogonal decomposition to the ensemble data. This information is then applied to train neural networks which is capable of predicting POD dynamics over a broad range of oscillating amplitudes. In addition to open-loop neural network, a closed-loop network, using long-short term network, is applied to develop a reduced order model of lift coefficient through integration of pressure modes. The results are validated with those obtained from UVLM simulations.

Original languageEnglish
Title of host publicationAIAA SciTech Forum and Exposition, 2023
DOIs
StatePublished - 2023
EventAIAA SciTech Forum and Exposition, 2023 - Orlando, United States
Duration: 23 Jan 202327 Jan 2023

Publication series

NameAIAA SciTech Forum and Exposition, 2023

Conference

ConferenceAIAA SciTech Forum and Exposition, 2023
Country/TerritoryUnited States
CityOrlando
Period23/01/2327/01/23

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