Particle Image Velocimetry Refinement via Consensus ADMM

TL;DR

This paper introduces a consensus-based refinement method for Particle Image Velocimetry that combines multiple algorithms using ADMM, improving flow quantification accuracy and robustness while maintaining real-time performance.

Contribution

It proposes a novel consensus framework using ADMM to integrate multiple flow estimation algorithms, enhancing accuracy and robustness in PIV measurements.

Findings

Achieves up to 20% reduction in end-point-error

Operates at 60Hz inference rate

Enhances performance with outlier rejection

Abstract

Particle Image Velocimetry (PIV) is an imaging technique in experimental fluid dynamics that quantifies flow fields around bluff bodies by analyzing the displacement of neutrally buoyant tracer particles immersed in the fluid. Traditional PIV approaches typically depend on tuning parameters specific to the imaging setup, making the performance sensitive to variations in illumination, flow conditions, and seeding density. On the other hand, even state-of-the-art machine learning methods for flow quantification are fragile outside their training set. In our experiments, we observed that flow quantification would improve if different tunings (or algorithms) were applied to different regions of the same image pair. In this work, we parallelize the instantaneous flow quantification with multiple algorithms and adopt a consensus framework based on the alternating direction method of multipliers, seamlessly incorporating priors such as smoothness and incompressibility. We perform several numerical experiments to demonstrate the benefits of this approach. For instance, we achieve a decrease in end-point-error of up to 20% of a dense-inverse-search estimator at an inference rate of 60Hz, and we show how this performance boost can be increased further with outlier rejection. Our method is implemented in JAX, effectively exploiting hardware acceleration, and integrated in Flow Gym, enabling (i) reproducible comparisons with the state-of-the-art, (ii) testing different base algorithms, (iii) straightforward deployment for active fluids control applications.