Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil

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.

Automated summary

An artificial neural network-based reduced order model was developed to predict hydrodynamics forces on an airfoil at different angles of attack using proper orthogonal decomposition. The model was trained using pressure coefficients and successfully validated on both in-sample and out-of-sample data, demonstrating good agreement with true numerical data.