Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Zhou K, Li J, Hong J, Grauer SJ (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Measurement Science and Technology

Book Volume: 34

Article Number: 065302

Journal Issue: 6

DOI: 10.1088/1361-6501/acc049

Abstract

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier-Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods.

Involved external institutions

Pennsylvania State University (Penn State) US United States (USA) (US) University of Minnesota (UMN) US United States (USA) (US)

How to cite

APA:

Zhou, K., Li, J., Hong, J., & Grauer, S.J. (2023). Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments. Measurement Science and Technology, 34(6). https://dx.doi.org/10.1088/1361-6501/acc049

MLA:

Zhou, Ke, et al. "Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments." Measurement Science and Technology 34.6 (2023).

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