Sensor-based Temporal Super-Resolution of non-Time-Resolved Flowfields using Deep Learning

This work introduces a novel method to estimate time-resolved (TR) velocity flow-fields from undersampled Particle Image Velocimetry (PIV) measurements and oversampled pressure sensor data. Feedforward and long short-term memory neural networks are utilized to estimate the temporal evolution of a low-dimensional proper orthogonal decomposition (POD) subspace. Unlike conventional techniques that correlate non-TR sensor and POD states, the proposed method leverages available TR sensor history to encode potentially missing correlations into the estimator. The efficacy of the method is demonstrated for laminar flow simulations of two side-by-side cylinders, which is characterized by highly non-linear wake interactions. The technique is also tested on a high Reynolds number turbulent separated flow over a Gaussian speed-bump validation geometry. Our results show promise in advancing sensor-based estimation in two avenues: (i) Educing unresolved dynamics through the estimated fields; (ii) Real-time sensing of turbulent flows made possible with the use of low-order subspaces and oversampled pressure sensors.