Extrapolating Fluid Flow Simulations From Pressure Measurements

A team of researchers from Clemson University has developed a new method that allows the reconstruction of fluid flow patterns based solely on pressure measurements taken on the surface of an object immersed in the fluid. This groundbreaking technique draws inspiration from the lateral lines found in fish, which enable them to sense disturbances and understand flow patterns using pressure and velocity sensors. By combining dynamic mode decomposition (DMD), a method that approximates unsteady flows by modeling them as a superposition of linear modes, with deep neural networks, the researchers were able to accurately reconstruct flow fields from pressure measurements.

The team successfully demonstrated the effectiveness of their methodology in various fluid-structure interaction scenarios, including free oscillations in the wake of a cylinder and forced oscillations of tandem cylinders. The reconstructed pressure and velocity fields showed a high level of accuracy, capturing essential flow characteristics and vortex wake advection downstream of the cylinder.

This research has significant implications for fields such as underwater robotics, where understanding the surrounding fluid environment is crucial for tasks like obstacle avoidance and optimal motion planning. The ability to estimate flow fields from surface pressure measurements opens up possibilities for near real-time sensing and control of local flow and structural responses. The findings of this study provide a framework for a more comprehensive understanding of fluid dynamics based solely on surface measurements, which has been a challenging problem due to the high dimension and unsteady nature of fluid flow.

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