Pressure from data-driven-estimated velocity fields using snapshot PIV and fast probes

This work explores the use of data-driven techniques to retrieve time-resolved information from snapshot PIV by exploiting the information from synchronized high-repetition rate sensors measuring flow quantities in few points, and to compute from it the instantaneous pressure field leveraging the Navier-Stokes momentum equation of the flow. This work focus on a technique rooted in the Extended Proper Orthogonal Decomposition, which already proven good performances in estimating time-resolved velocity fields from a finite number of probes synchronized with field measurements. The performances of the technique and its robustness to noise are tested on 2 synthetic dataset, a laminar one and a turbulent one, and compared to the most commonly applied technique to retrieve time-resolved information from snapshot PIV which exploits Taylor’s hypothesis.