Global Flow Reconstruction from Local Pressure Data using Dynamic Mode Decomposition

Inspired by the lateral lines of fish, sensing aspects of a fluid flow using measurements on a surface has become a topic of substantial interest. However, current approaches often use analytical methods that are only applicable to steady potential flows, or otherwise use machine learning to estimate specific flow parameters. The recent development of Dynamic Mode Decomposition (DMD) has allowed the parameterization of dynamic features of the entire flow. In this talk, we investigate the application of deep neural networks to infer the DMD modes of the pressure field in a large and unsteady fluid-body interaction problem, using only a time-series of pressure measurements on an obstacle. These modes can then be used to reconstruct the entire flowfield. This work has potential applications in identifying efficient trajectories through unsteady flows and in obstacle sensing.