Advection-based multiframe iterative correction for pressure estimation from velocity fields

TL;DR

This paper introduces an advection-based iterative method to enhance pressure field estimation from velocity data, effectively reducing noise and correcting errors by leveraging temporal and spatial information.

Contribution

The proposed technique innovatively combines advection modeling with iterative smoothing to improve pressure estimation accuracy from velocity fields.

Findings

Outperforms conventional filters in velocity and pressure accuracy.

Effective in reducing spatially coherent errors.

Validated on synthetic and experimental datasets.

Abstract

A novel method to improve the accuracy of pressure field estimation from time-resolved Particle Image Velocimetry data is proposed. This method generates several new time-series of velocity field by propagating in time the original one using an advection-based model, which assumes that small-scale turbulence is advected by large-scale motions. Then smoothing is performed at the corresponding positions across all the generated time-series. The process is repeated through an iterative scheme. The proposed technique smears out spatial noise by exploiting time information. Simultaneously, temporal jitter is repaired using spatial information, enhancing the accuracy of pressure computation via the Navier-Stokes equations. We provide a proof of concept of the method with synthetic datasets based on a channel flow and the wake of a 2D wing. Different noise models are tested, including Gaussian white noise and errors with some degree of spatial coherence. Additionally, the filter is evaluated on an experimental test case of the wake of an airfoil, where pressure field ground truth is not available. The result shows the proposed method performs better than conventional filters in velocity and pressure field estimation, especially when spatially coherent errors are present. The method is of direct application in advection-dominated flows, although its extension with more advanced models is straightforward.