Supplementary material to "Glacier Image Velocimetry: an open-source toolbox for easy and rapid calculation of high-resolution glacier-velocity fields"

Abstract. We present Glacier Image Velocimetry (GIV), an open-source and easy-to-use software toolkit for rapidly calculating high-spatial-resolution glacier velocity fields. Glacier ice velocity fields reveal flow dynamics, ice-flux changes, and (with additional data and modelling) ice thickness. Obtaining glacier velocity measurements over wide areas with field techniques is labour intensive and often associated with safety risks. The recent increased availability of high-resolution, short-repeat-time optical imagery allows us to obtain ice displacement fields using “feature tracking” based on matching persistent irregularities on the ice surface between images and hence, surface velocity over time. GIV is fully parallelized and automatically detects, filters, and extracts velocities from large datasets of images. Through this coupled toolchain and an easy-to-use GUI, GIV can rapidly analyse hundreds to thousands of image pairs on a laptop or desktop computer. We present four example applications of the GIV toolkit in which we complement a glaciology field campaign (Glaciar Perito Moreno, Argentina) and calculate the velocity fields of small mid-latitude (Glacier d'Argentière, France) and tropical glaciers (Volcán Chimborazo, Ecuador), as well as very large glaciers (Vavilov Ice Cap, Russia). Fully commented MATLAB code and a stand-alone app for GIV are available from GitHub and Zenodo (see https://doi.org/10.5281/zenodo.4624831, Van Wyk de Vries, 2021a).

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Glacier Image Velocimetry: an open-source toolbox for easy and rapid calculation of high-resolution glacier-velocity fields

10.5194/tc-2020-204 ◽

2020 ◽

Author(s):

Maximillian Van Wyk de Vries ◽

Andrew D. Wickert

Keyword(s):

High Resolution ◽

Open Source ◽

Velocity Fields ◽

Displacement Fields ◽

Ice Cap ◽

Ice Surface ◽

Image Velocimetry ◽

Repeat Time ◽

Glacier Velocity ◽

Image Pairs

Abstract. We present Glacier Image Velocimetry (GIV), an open-source and easy-to-use tool for rapidly calculating high spatial and temporal resolution glacier-velocity fields. Glaciers' velocity fields reveal their flow dynamics, stability, and thickness. Obtaining widespread glacier-velocity measurements in the field is challenging and labour intensive. Recent increases in the availability of high-resolution, short-repeat-time optical imagery improve this, as persistent irregularities on the ice surface allow us to use feature tracking – an accidental form of particle image velocimetry to obtain displacement fields, and hence, velocity over time. While these techniques have been used to calculate velocity fields for many glaciers, existing toolboxes can be expensive, complex or inflexible to use. GIV is fully parallelized, and automatically detects, filters, and extracts velocities from large datasets of images. Through this coupled toolchain and an easy-to-use GUI, GIV can rapidly analyse hundreds to thousands of image pairs on any modern laptop or desktop. We present four examples of how this model may be used: to complement a glaciology field campaign (Glaciar Perito Moreno, Argentina), calculate the velocity fields of small (Glacier d’Argentière, France) and very large (Vavilov ice cap, Russia) glaciers, and determine the ice volume present within a tropical ice cap (Volcán Chimborazo, Ecuador). Fully commented code and a standalone app for GIV are available from GitHub and Zenodo.

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Experimental Validation of Falling Liquid Film Models: Velocity Assumption and Velocity Field Comparison

Polymers ◽

10.3390/polym13081205 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1205

Author(s):

Ruiqi Wang ◽

Riqiang Duan ◽

Haijun Jia

Keyword(s):

Experimental Data ◽

Particle Image Velocimetry ◽

Velocity Field ◽

Experimental Validation ◽

Particle Image ◽

Velocity Fields ◽

Number Range ◽

Comparison Results ◽

Image Velocimetry ◽

Experimental Particle

This publication focuses on the experimental validation of film models by comparing constructed and experimental velocity fields based on model and elementary experimental data. The film experiment covers Kapitza numbers Ka = 278.8 and Ka = 4538.6, a Reynolds number range of 1.6–52, and disturbance frequencies of 0, 2, 5, and 7 Hz. Compared to previous publications, the applied methodology has boundary identification procedures that are more refined and provide additional adaptive particle image velocimetry (PIV) method access to synthetic particle images. The experimental method was validated with a comparison with experimental particle image velocimetry and planar laser induced fluorescence (PIV/PLIF) results, Nusselt’s theoretical prediction, and experimental particle tracking velocimetry (PTV) results of flat steady cases, and a good continuity equation reproduction of transient cases proves the method’s fidelity. The velocity fields are reconstructed based on different film flow model velocity profile assumptions such as experimental film thickness, flow rates, and their derivatives, providing a validation method of film model by comparison between reconstructed velocity experimental data and experimental velocity data. The comparison results show that the first-order weighted residual model (WRM) and regularized model (RM) are very similar, although they may fail to predict the velocity field in rapidly changing zones such as the front of the main hump and the first capillary wave troughs.

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Analysis of local velocity gradients in rapid mixer using particle image velocimetry technique

Water Science & Technology Water Supply ◽

10.2166/ws.2002.0149 ◽

2002 ◽

Vol 2 (5-6) ◽

pp. 47-55

Author(s):

N.-S. Park ◽

H. Park

Keyword(s):

Particle Image Velocimetry ◽

Particle Image ◽

Velocity Fields ◽

Local Velocity ◽

Mixing Condition ◽

Particle Image Velocimetry Technique ◽

Velocity Gradients ◽

Image Velocimetry ◽

Current Design ◽

G Value

Recognizing the significance of factual velocity fields in a rapid mixer, this study focuses on analyzing local velocity gradients in various mixer geometries with particle image velocimetry (PIV) and comparing the results of the analysis with the conventional G-value, for reviewing the roles of G-value in the current design and operation practices. The results of this study clearly show that many arguments and doubts are possible about the scientific correctness of G-value, and its current use. This is because the G-value attempts to represent the turbulent and complicated factual velocity field in a jar. Also, the results suggest that it is still a good index for representing some aspects of mixing condition, at least, mixing intensity. However, it cannot represent the distribution of velocity gradients in a jar, which is an important factor for mixing. This study as a result suggests developing another index for representing the distribution to be used with the G-value.

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An Air Curtain Along a Wall With High Inlet Turbulence

Journal of Fluids Engineering ◽

10.1115/1.1758265 ◽

2004 ◽

Vol 126 (3) ◽

pp. 391-398 ◽

Cited By ~ 5

Author(s):

Brandon S. Field ◽

Eric Loth

Keyword(s):

Ambient Air ◽

Reynolds Numbers ◽

Velocity Fields ◽

Air Curtain ◽

Image Velocimetry ◽

Number Variation ◽

Inflow Turbulence ◽

High Turbulence ◽

Isothermal Wall ◽

Eddy Dynamics

A downward blowing isothermal wall jet at moderate Reynolds numbers (1,500 to 8,500) with significant inflow turbulence (ca. 6%) was investigated. The flow configuration is an idealization of the air curtains of refrigerated display cases. Flow visualization using particle seeding was employed to identify the flow field eddy dynamics. Particle Image Velocimetry was used to examine the velocity fields in terms of mean and fluctuating values. These diagnostics showed that the air curtain entrainment was dominated by a large variety of eddies that engulfed ambient air into the air curtain. The velocity fields generally showed linear spreading, significant deceleration and high turbulence levels (ca. 25%). It was observed that the air curtain dynamics, velocity fields and growth were not significantly sensitive to Reynolds number variation between Re=3,800 and Re=8,500. However, at low air velocities (Re=1,500), the curtain was found to detach, leading to a large air curtain thickness and high curtain entrainment.

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