Hydrokinetic turbine wake flow reconstruction in large-scale waterways using physics-informed convolutional neural networks

We developed a physics-informed autoencoder convolutional neural network (CNN) to reconstruct the 3D time-averaged velocity field and turbulence kinetic energy of the wake flow of hydrokinetic turbines using the instantaneous high-fidelity simulation data. To ensure the prediction results follow the physics laws, mass and momentum conservation equations are embedded into the loss function of the CNN model. The CNN is trained using the large eddy simulation (LES) result of the wake flow of a single row of turbines. Then the validation is conducted using the LES results of overlapping and staggered cases of two rows of turbines. The results show good agreement between the LES and CNN algorithms while the CNN requires only a small fraction of the computational costs of the LES.