High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network

Abstract

In this study, a deep learning-based approach is applied with the aim of reconstructing high-resolution turbulent flow fields using minimal flow field data. A multi-scale enhanced super-resolution generative adversarial network with a physics-based loss function is introduced as a model to reconstruct the high-resolution flow fields. The model capability to reconstruct high-resolution laminar flows is examined using direct numerical simulation data of laminar flow around a square cylinder. The results reveal that the model can accurately reproduce the high-resolution flow fields even when limited spatial information is provided. The DNS data of turbulent channel flow at two friction Reynolds numbers Reτ=180 and 550 are used to assess the ability of the model to reconstruct the high-resolution wall-bounded turbulent flow fields. The instantaneous and statistical results obtained from the model agree well with the ground truth data, indicating that the model can successfully learn to map the coarse flow fields to the high-resolution ones. Furthermore, the possibility of performing transfer learning for the case of turbulent channel flow is thoroughly examined. The results indicate that the amount of the training data and the time required for training can be effectively reduced without affecting the performance of the model. The computational cost of the proposed model is also found to be effectively low. These results demonstrate that using high-fidelity training data with physics-guided generative adversarial network-based models can be practically efficient in reconstructing high-resolution turbulent flow fields from extremely coarse data.