Kavli Affiliate: Morteza Gharib
| First 5 Authors: Peter I Renn, Cong Wang, Sahin Lale, Zongyi Li, Anima Anandkumar
| Summary:
We apply Fourier neural operators (FNOs), a state-of-the-art operator
learning technique, to forecast the temporal evolution of experimentally
measured velocity fields. FNOs are a recently developed machine learning method
capable of approximating solution operators to systems of partial differential
equations through data alone. The learned FNO solution operator can be
evaluated in milliseconds, potentially enabling faster-than-real-time modeling
for predictive flow control in physical systems. Here we use FNOs to predict
how physical fluid flows evolve in time, training with particle image
velocimetry measurements depicting cylinder wakes in the subcritical vortex
shedding regime. We train separate FNOs at Reynolds numbers ranging from Re =
240 to Re = 3060 and study how increasingly turbulent flow phenomena impact
prediction accuracy. We focus here on a short prediction horizon of ten
non-dimensionalized time-steps, as would be relevant for problems of predictive
flow control. We find that FNOs are capable of accurately predicting the
evolution of experimental velocity fields throughout the range of Reynolds
numbers tested (L2 norm error < 0.1) despite being provided with limited and
imperfect flow observations. Given these results, we conclude that this method
holds significant potential for real-time predictive flow control of physical
systems.
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