Time-resolved reconstruction of flow field around a circular cylinder by recurrent neural networks based on non-time-resolved particle image velocimetry measurements

Abstract

Particle image velocimetry (PIV) has been extensively used in wind-tunnel test for flow-field measurement. However, the sampling frequency of traditional PIV is low and physics of flow field in high-frequency range is hard to capture. A data processing approach is proposed to obtain time-resolved flow field around a circular cylinder with PIV measurement data (high spatial precision) and wind speed measurement data using probes (high time resolution at discrete downstream locations). Proper orthogonal decomposition (POD) is used to extract the compact spatial representations of the flow field based on PIV measurement data, and bidirectional recurrent neural networks (RNNs) with gated recurrent units are designed to learn the time sequences of coefficients of the first few POD modes based on both PIV and probe measurement data. We analyze qualitatively the relationship between the velocity time sequence and the spatial distribution of velocity. Based on this qualitative relationship, an RNN with a unique “many-to-one” architecture is designed to learn the coefficients and make use of the intrinsic property that the velocity time sequence contains information on the spatial distribution of velocity. The inputs to the RNN are the sequential velocity-probe measurements with high sampling frequency, and the outputs are the coefficients of the first few POD modes. The proposed approach is validated by using both simulated datasets for two low Reynolds numbers (200 and 500) and experimental dataset for Reynolds number of 2.4 × 104. We also investigate the influence of velocity time length used in the inputs as well as the number and distribution of velocity probes on prediction accuracy. This study provides a feasible approach to get time-resolved flow field with high accuracy while low cost for all Reynolds numbers.