Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network

Abstract

The modeling of wake effects plays an essential role in wind farm optimal design and operation. In this study, a novel deep learning method, called Super-Fidelity Network (SFNet), is proposed for wind farm wake modeling, which would be the first attempt to combine the advantages of both analytical models and numerical models through deep learning methods. Specifically, the low-fidelity flow fields generated by the analytical models serve as the prior information for predicting high-fidelity flow fields. Then the SFNet learns the mapping relationships between low-fidelity data and high-fidelity data, thereby predicting high-fidelity flow fields without resorting to huge computational resources. Numerical experiments demonstrate that the mean absolute error of the developed model is just 1.9% with respect to the freestream wind speed when compared with high-fidelity data, after trained on only 45 samples. In addition, the generalizability of the proposed SFNet in yaw angles, wind speeds and array column extensions is verified by a series of numerical experiments. Furthermore, the experimental results demonstrate that the trained model is able to predict the flow field of a wind farm consisting of 100 turbines within several seconds based on a standard desktop.