Data-driven prediction of unsteady loading on a 2D deforming airfoil

In this study, we use a data-driven model enhanced with a design gate to predict the unsteady pressure and shear distributions along a 2D deforming airfoil. The deformation of the airfoil is governed by several design parameters, leading to an additional design space on top of the manifolds of the dynamical system. To generate training data for our data-driven approach, we extract unsteady traction distributions around deforming airfoils from numerical flow simulations across the design space. Subsequently, this dataset is used to build our data-driven model. The model relies on a pLSTM network architecture, which is a new variant of traditional LSTMs that embeds a design gate in the pLSTM cell. This new architecture helps overcome the well-known stability problems of LSTM, allows switching design conditions during the model operation, and increases the learning capacity of the neural network model for complex design spaces. We demonstrate that this data-driven model can predict the evolution of the surface loading for different deformation histories. Therefore, this problem can be combined with a structural model to perform two-way fluid-structure interaction problems in the future. Finally, at the end of the talk, we briefly consider how to extend other architectures like GRUs and transformers with design gates to improve their ability to dynamically predict flows across design space parameters.