Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil
Hamayun Farooq,
Ahmad Saeed,
Imran Akhtar,
Zafar Bangash
Affiliations
Hamayun Farooq
Center for Advanced Studies in Pure and Applied Mathematics (CASPAM), Bahauddin Zakariya University (BZU), Multan 60800, Pakistan
Ahmad Saeed
Department of Mechanical Engineering, NUST College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Islamabad 44000, Pakistan
Imran Akhtar
Department of Mechanical Engineering, NUST College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Islamabad 44000, Pakistan
Zafar Bangash
Department of Mechanical Engineering, NUST College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Islamabad 44000, Pakistan
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 attack (AOA) and past POD coefficients as an input and predicts the future temporal coefficients. We also decompose the surface pressure modes into lift and drag components. These surface pressure modes are then employed to calculate the pressure component of lift CLp and drag CDp coefficients. The train model is then tested on the in-sample data and out-of-sample data. The results show good agreement with the true numerical data, thus validating the neural network based model.
