Uncertainty-based pressure field reconstruction from PIV/PTV flow measurements with generalized least-squares
Jiacheng Zhang, Sayantan Bhattacharya, and Pavlos P. Vlachos

TL;DR
This paper introduces an uncertainty-aware pressure reconstruction method from PIV/PTV velocity data, significantly improving accuracy by incorporating error estimates into a generalized least-squares framework.
Contribution
It presents a novel GLS-based pressure reconstruction approach that accounts for velocity measurement uncertainties, enhancing pressure field accuracy over existing methods.
Findings
Error reduction of up to 250% compared to baseline methods.
Median absolute pressure errors decreased by as much as 96%.
Method is robust to velocity errors and spatial correlations.
Abstract
A novel uncertainty-based pressure reconstruction method is proposed to evaluate the instantaneous pressure fields from velocity fields measured using particle image velocimetry (PIV) or particle tracking velocimetry (PTV). First, the pressure gradient fields are calculated from velocity fields, while the local and instantaneous pressure gradient uncertainty is estimated from the velocity uncertainty using a linear-transformation based algorithm. The pressure field is then reconstructed by solving an overdetermined linear system which involves the pressure gradients and boundary conditions. This linear system is solved with generalized least-squares (GLS) which incorporates the previously estimated variances and covariances of the pressure gradient errors as inverse weights to optimize the reconstructed pressure field. The method was validated with synthetic velocity fields of a 2D…
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Taxonomy
TopicsFlow Measurement and Analysis · Fluid Dynamics and Turbulent Flows · Advanced Sensor Technologies Research
