Special issue on uncertainty quantification in particle image velocimetry and Lagrangian particle tracking

Sciacchitano, Andrea; Discetti, Stefano

doi:10.1088/1361-6501/ac2c49

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Efforts to extract accurate and minimally-invasive velocimetry data from a system form the field of PTV algorithm research. PTV algorithm research features investigations with single-camera views, (Fischer et al, 2022;Hou et al, 2021;Noto et al, 2021) novel experimental setups, (Bautista-Capetillo et al, 2014;Choi et al, 2012;Dal Sasso et al, 2018;de Cerqueira et al, 2023;Guo et al, 2014;Landauer et al, 2019;Mujtaba and de Lima, 2018;Parker et al, 2022;Sabbagh et al, 2020;Sokoray-Varga and Józsa, 2008) and analysis of numerical methods, (Azadi et al, 2021;Cai et al, 2021;Janke et al, 2020;Lei et al, 2012;Mallery et al, 2020;Patel et al, 2018;Puzyrev et al, 2020;Qureshi et al, 2021;Sciacchitano and Discetti, 2022;Tauro et al, 2019;Toeneboehn et al, 2018;Zhao et al, 2020) all requiring simplified, yet sufficiently descriptive, experimental flow geometries as testbeds for the assessment of algorithm performance.…”

Section: Introductionmentioning

confidence: 99%

Single-camera PTV within interfacially sheared drops in microgravity

McMackin,

Adam,

Riley

et al. 2023

Exp Fluids

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Development of experimental methods for in situ particle tracking velocimetry (PTV) is fundamental for allowing measurement of moving systems nontailored for velocimetry. This investigation focuses on the development of a post-processing methodology for single-camera PTV, without laser-sheet illumination, tracking native air-bubbles as tracer particles within a liquid drop of human insulin in microgravity. This PTV scenario was facilitated by microgravity technology known as the ring-sheared drop (RSD), aboard the International Space Station, which produced an optical imaging scenario and fluid flow geometry suitable as a testbed for PTV research. The post processing methodology performed included five steps: (i) physical system characterization and native air-bubble tracer identification, (ii) image projection and single-camera calibration, (iii) depth determination and 3D particle position determination, (iv) ray tracing and refraction correction, and (v) particle history and dataIn situ PTV in sheared drops expansion for suboptimal particles. Overall, this post processing methodology successfully allowed for a total of 1,085 particle measurements in a scenario where none were previously possible. Such post processing methodologies have promise for application to other in situ PTV scenarios allowing better understanding of physical systems whose flow is difficult to measure and or where PTV-specific optical elements (such as laser lights sheets and dual camera setups) are not permissible due to physical or safety constraints.

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Section: Introductionmentioning

confidence: 99%

Single-camera PTV within interfacially sheared drops in microgravity

McMackin,

Adam,

Riley

et al. 2023

Exp Fluids

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A deep neural network architecture for reliable 3D position and size determination for Lagrangian particle tracking using a single camera

Ratz

Sachs

König

et al. 2023

Meas. Sci. Technol.

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Microfluidic flows feature typically fully three-dimensional velocity fields. However, often the optical access for measurements is limited. Astigmatism or defocus particle tracking velocimetry is a technique that enables the 3D position determination of individual particles by the analysis of astigmatic/defocused particle images. The classification and position determination of particles is a task well suited to deep neural networks (DNNs). In this work, two DNNs are used to extract the class and in-plane position (object detection) as well as the depth position (regression network). The performance of both DNNs is assessed by the position uncertainties as well as the precision of the size classes and the amount of recalled particles. The DNNs are evaluated on a synthetic dataset and establish a new benchmark of DNNs in defocus tracking applications. The recall is higher than compared to classic methods and the in-plane errors are always subpixel accurate. The relative uncertainty in the depth position is below \SI{1}{\percent} for all examined particle seeding concentrations. Additionally, the performance on experimental images, using four different particle sizes, ranging from 1.14 to \SI{5.03}{\micro\meter} is analyzed. The particle images are systematically rearranged to produce comprehensive datasets of varying particle seeding concentrations. The distinction between particles of similar size is more challenging but the DNNs still show very good results. A precision above \SI{96}{\percent} is reached with a high recall above \SI{95}{\percent}. The error in the depth position remains below \SI{1}{\percent} and the in-plane errors are subpixel accurate with respect to the labels. The work shows that first, DNNs can be trained with artificially rearranged data sets based on individual experimental images and are therefore easily adaptable to various experimental setups and applicable by non experts. Second, the DNNs can be successfully adapted to determine additional variables as in this case the size of the suspended particles.

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Single-camera PTV within interfacially sheared drops in microgravity

McMackin

Adam

Riley

et al. 2023

Preprint

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Development of experimental methods for in situ particle tracking velocimetry (PTV) is fundamental for allowing measurement of moving systems non-tailored for velocimetry. This investigation focuses on the development of a post-processing methodology for single-camera PTV, without laser-sheet illumination, tracking native air-bubbles as tracer particles within a liquid drop of human insulin in microgravity. This PTV scenario was facilitated by microgravity technology known as the ring-sheared drop (RSD), aboard the International Space Station, which produced an optical imaging scenario and fluid flow geometry suitable as a testbed for PTV research. The post processing methodology performed included five steps: (i) physical system characterization and native air-bubble tracer identification, (ii) image projection and single-camera calibration, (iii) depth determination and 3D particle position determination, (iv) ray tracing and refraction correction, and (v) particle history and data expansion for suboptimal particles. Overall, this post processing methodology successfully allowed for a total of 1,085 particle measurements in a scenario where none were previously possible. Such post processing methodologies have promise for application to other in situ PTV scenarios allowing better understanding of physical systems whose flow is difficult to measure and or where PTV-specific optical elements (such as laser lights sheets and dual camera setups) are not permissible due to physical or safety constraints.

show abstract

Special issue on uncertainty quantification in particle image velocimetry and Lagrangian particle tracking

Cited by 3 publications

References 13 publications

Single-camera PTV within interfacially sheared drops in microgravity

Single-camera PTV within interfacially sheared drops in microgravity

A deep neural network architecture for reliable 3D position and size determination for Lagrangian particle tracking using a single camera

Single-camera PTV within interfacially sheared drops in microgravity

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