Reservoir computing reduced-order model based on particle image velocimetry data of post-stall flow

Abstract

The present study proposes a reservoir computing reduced-order model (RCROM) of the post-stall flow around the National Advisory Committee for Aeronautics 0015 airfoil based on the time series velocity field, and the estimation accuracy of the RCROM is evaluated compared to that of a linear reduced-order model (LROM). The data were experimentally obtained by particle image velocimetry at a chord Reynolds number of 6.4 × 104 and an angle of attack of 18°. The low-dimensional description of the velocity field can be obtained by decomposing the velocity field with a proper orthogonal decomposition (POD) technique and by employing the leading POD mode coefficients as temporal variables of the data instead of the velocity field. Reservoir computing (RC) is adopted as a nonlinear function that predicts several steps ahead of the leading POD mode coefficients. The hyperparameters of RC are tuned by Bayesian optimization, and the optimized RCROM outperforms the LROM in terms of estimation accuracy. The estimation accuracy of the RCROM can be investigated under different numbers of the predicted dominant POD modes and prediction step conditions. As a result, the RCROM shows higher estimation accuracy than the LROM.