An algorithm to estimate unsteady and quasi-steady pressure fields from velocity field measurements

TL;DR

This paper presents a novel, efficient algorithm for estimating unsteady and quasi-steady pressure fields from velocity measurements, reducing computational cost and error accumulation, validated through simulations and real PIV data.

Contribution

The method introduces median polling along measured velocity nodes, eliminating interpolation and lowering computational costs compared to previous techniques.

Findings

Accurately estimates pressure fields from velocity data.

Reduces computational time and error accumulation.

Successfully applied to both simulated and empirical flow data.

Abstract

We describe and characterize a method for estimating the pressure field corresponding to velocity field measurements, such as those obtained by using particle image velocimetry. The pressure gradient is estimated from a time series of velocity fields for unsteady calculations or from a single velocity field for quasi-steady calculations. The corresponding pressure field is determined based on median polling of several integration paths through the pressure gradient field in order to reduce the effect of measurement errors that accumulate along individual integration paths. Integration paths are restricted to the nodes of the measured velocity field, thereby eliminating the need for measurement interpolation during this step and significantly reducing the computational cost of the algorithm relative to previous approaches. The method is validated by using numerically-simulated flow past a stationary, two-dimensional bluff body and a computational model of a three-dimensional, self-propelled anguilliform swimmer to study the effects of spatial and temporal resolution, domain size, signal-to-noise ratio, and out-of-plane effects. Particle image velocimetry measurements of a freely-swimming jellyfish medusa and a freely-swimming lamprey are analyzed using the method to demonstrate the efficacy of the approach when applied to empirical data.