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Workshop: Machine Learning and the Physical Sciences

Predicting Full-Field Turbulent Flows Using Fourier Neural Operator

Peter Renn · Sahin Lale · Cong Wang · Zongyi Li · Anima Anandkumar · Morteza Gharib

Abstract: We present an experimental application of Fourier neural operators (FNOs) for predicting the temporal development of wakes behind tandem bluff body arrangements at a Reynolds number of $Re \approx 1500$. FNOs are recently introduced tools in machine learning capable of approximating solution operators to partial differential equations, such as the Navier-Stokes equations, through data alone. Once trained, FNOs can predict full-field solutions in milliseconds. Here we apply this method to experimental velocity fields acquired via particle image velocimetry and compare the predicted temporal developments of the learned solution operator with the actual measurements taken at those timesteps. We find that FNOs are capable of accurately predicting wake developments hundreds of milliseconds into the future. Using several tandem cylinder configurations, we also demonstrate that learned solution operators are surprisingly capable of adapting to unseen conditions and generalizing wake dynamics across different arrangements.

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