An a posteriori data-driven method for phase-averaged optical measurements

TL;DR

This paper introduces a novel a posteriori data-driven method using ISOMAP to reconstruct phase information from uncorrelated snapshots, enabling phase-averaged flow analysis without synchronization, validated through simulations and experiments.

Contribution

The work presents a new approach that reconstructs phase information from uncorrelated images, eliminating the need for synchronized data acquisition in fluid dynamics measurements.

Findings

Accurately captures periodic flow features without synchronization.

Effective in both numerical simulations and experimental datasets.

Extends applicability to archival and hardware-limited datasets.

Abstract

Phase-averaging is a fundamental approach for investigating periodic and non-stationary phenomena. In fluid dynamics, these can be generated by rotating blades such as propellers/turbines or by pulsed jets. Traditional phase-averaging approaches often rely on synchronized data acquisition systems, which might require high-speed cameras, light sources, and precise delay generators and encoders, making them expensive and sometimes unfeasible. This work proposes an a posteriori data-driven approach that reconstructs phase information from randomly acquired uncorrelated photographic frames (snapshots) using the ISOMAP algorithm. The technique enables accurate reordering of snapshots in the phase space and subsequent computation of the phase-averaged flow field without the need for synchronization. The framework was validated through numerical simulations and experimental fluid dynamics datasets from an optical setup featuring single- and multi-propeller configurations. The results demonstrate that the proposed method effectively captures the periodic flow characteristics while addressing the challenges related to synchronization and hardware limitations. Furthermore, the ability to apply this technique to archival datasets extends its applicability to a wide range of experimental fluid dynamics studies. This approach provides a scalable and cost-effective alternative to traditional methods for the analysis of periodic phenomena.