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Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil
2021
Fluids
In this paper, an artificial neural network (ANN)-based reduced order model (ROM) is developed for the hydrodynamics forces on an airfoil immersed in the flow field at different angles of attack. The proper orthogonal decomposition (POD) of the flow field data is employed to obtain pressure modes and the temporal coefficients. These temporal pressure coefficients are used to train the ANN using data from three different angles of attack. The trained network then takes the value of angle of
doi:10.3390/fluids6090332
fatcat:m3tyafn65fdpjmyczztka65nua