TY - GEN
T1 - Recurrent-Neural Network Prediction of Lift on an Oscillating Plate
AU - Ahsan, Nida
AU - Ayyad, Mahmoud
AU - Hajj, Muhammad R.
AU - Akhtar, Imran
N1 - Publisher Copyright:
© 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.2514/6.2023-1435
DO - 10.2514/6.2023-1435
M3 - Conference contribution
AN - SCOPUS:85199103418
SN - 9781624106996
T3 - AIAA SciTech Forum and Exposition, 2023
BT - AIAA SciTech Forum and Exposition, 2023
T2 - AIAA SciTech Forum and Exposition, 2023
Y2 - 23 January 2023 through 27 January 2023
ER -