Deep learning reconstruction of high-Reynolds-number turbulent flow field around a cylinder based on limited sensors.

It is of significant importance to reconstruct the high-dimensional global flow field using limited sensor data, considering that sensors are often local and limited in full-scale measurements. In the literature, the reconstruction of laminar flows at low Reynolds number (Re) was mostly investigated. This study focuses on high-Re turbulent flows around a body, and aims to systematically investigate the reconstruction performance of deep learning models, including multilayer perceptron (MLP), convolutional neural network (CNN), and generative adversarial neural network (GAN). The extensive assessment encompasses the prediction accuracy of the instantaneous, phase-averaged, and mean flow fields, and the training efficiency. Results shows that GAN demonstrates a obvious advantage in reconstructing small-scale vortex structures. MLP is slightly better at reconstructing time-averaged flow fields (3%) and has a significant advantage in reconstruction efficiency. We also examine the generalization ability by applying transfer learning at different Re. Additionally, sensitivity of sensor number, training data size, and optimization of sensor layout are studied to further augment the accuracy of flow field reconstruction. • The turbulent flow at high Reynolds number is reconstructed using limit sensors. • The pros and cons of deep learning methods are clarified. • GAN model has advantage in reconstructing small-scale vortex structures. [ABSTRACT FROM AUTHOR]