Brandon Hilliard

University of Idaho | Graduate Student

Subject Areas: Mechanical Engineering, Water Management, Experimental Fluid Dynamics

 Recent Activity

ABSTRACT:

Porous media flows are common in both natural and anthropogenic systems. Mapping these flows in a laboratory setting is challenging and often requires non-intrusive measurement techniques, such as particle image velocimetry (PIV) coupled with refractive index matching (RIM). RIM-coupled PIV allows the mapping of velocity fields around transparent solids by analyzing the movement of neutrally buoyant micron-sized seeding particles. The use of this technique in a porous medium can be problematic because seeding particles adhere to grains, which causes the grain bed to lose transparency and can obstruct pore flows. Another non-intrusive optical technique, planar laser-induced fluorescence (PLIF), can be paired with RIM and does not have this limitation because fluorescent dye is used instead of particles, but it has been chiefly used for qualitative flow visualization. Here, we propose a quantitative PLIF-based methodology to map both porous media flow fields and porous media architecture. Velocity fields are obtained by tracking the advection-dominated movement of the fluorescent dye plume front within a porous medium. We also propose an automatic tracking algorithm that quantifies 2D velocity components as the plume moves through space in both an Eulerian and a Lagrangian framework. We apply this algorithm to three data sets: a synthetic data set and two laboratory experiments. Performance of this algorithm is reported by the mean (bias error, B) and standard deviation (random error, SD) of the residuals between its results and the reference data. For the synthetic data, the algorithm produces maximum errors of B & SD = 32% & 23% in the Eulerian framework, respectively, and B & SD = −0.04% & 3.9% in the Lagrangian framework. The small-scale laboratory experimental data requires the Eulerian framework and produce errors of B & SD = −0.5% & 33%. The Lagrangian framework is used on the large-scale laboratory experimental data and produces errors of B & SD = 5% & 44%. Mapping the porous media architecture shows negligible error for reconstructing calibration grains of known dimensions. Article DOI: 10.1088/1361-6501/acfb2b

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ABSTRACT:

Porous media are ubiquitous, a key component of the water cycle and locus of many biogeochemical transformations. Mapping media architecture and interstitial flows have been challenging because of the inherent difficulty of seeing through solids. Previous works used particle image velocimetry (PIV) coupled with refractive index-matching (RIM) to quantify interstitial flows, but they were limited to specialized and often toxic fluids that precluded investigating biological processes. To address this limitation, we present a low-cost and scalable method based on RIM coupled PIV (RIM-PIV) and planar laser induced fluorescence (RIM-PLIF) to simultaneously map both media architecture and interstitial velocities. Here, we store and report the data used in "A biologically friendly, low-cost and scalable method to map permeable media architecture and interstitial flow" by Hilliard et al., 2020, in Geophysical Review Letters, DOI: 10.1029/2020GL090462

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Data for Mapping Permeable Media Architecture and Interstitial Flow
Created: Jan. 13, 2020, 6:37 p.m.
Authors: Hilliard, Brandon · Reeder, W. Jeffery · Richard S. Skifton · Ralph Budwig · William Basham · Tonina, Daniele

ABSTRACT:

Porous media are ubiquitous, a key component of the water cycle and locus of many biogeochemical transformations. Mapping media architecture and interstitial flows have been challenging because of the inherent difficulty of seeing through solids. Previous works used particle image velocimetry (PIV) coupled with refractive index-matching (RIM) to quantify interstitial flows, but they were limited to specialized and often toxic fluids that precluded investigating biological processes. To address this limitation, we present a low-cost and scalable method based on RIM coupled PIV (RIM-PIV) and planar laser induced fluorescence (RIM-PLIF) to simultaneously map both media architecture and interstitial velocities. Here, we store and report the data used in "A biologically friendly, low-cost and scalable method to map permeable media architecture and interstitial flow" by Hilliard et al., 2020, in Geophysical Review Letters, DOI: 10.1029/2020GL090462

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Data for Measuring porous media velocity fields and grain bed architecture with a quantitative PLIF-based technique
Created: March 7, 2023, 9:36 p.m.
Authors: Hilliard, Brandon · Budwig, Ralph · Richard S. Skifton · Vibhav Durgesh · William J. Reeder · Bhattarai, Bishal · Benjamin T. Martin · Tao Xing · Tonina, Daniele

ABSTRACT:

Porous media flows are common in both natural and anthropogenic systems. Mapping these flows in a laboratory setting is challenging and often requires non-intrusive measurement techniques, such as particle image velocimetry (PIV) coupled with refractive index matching (RIM). RIM-coupled PIV allows the mapping of velocity fields around transparent solids by analyzing the movement of neutrally buoyant micron-sized seeding particles. The use of this technique in a porous medium can be problematic because seeding particles adhere to grains, which causes the grain bed to lose transparency and can obstruct pore flows. Another non-intrusive optical technique, planar laser-induced fluorescence (PLIF), can be paired with RIM and does not have this limitation because fluorescent dye is used instead of particles, but it has been chiefly used for qualitative flow visualization. Here, we propose a quantitative PLIF-based methodology to map both porous media flow fields and porous media architecture. Velocity fields are obtained by tracking the advection-dominated movement of the fluorescent dye plume front within a porous medium. We also propose an automatic tracking algorithm that quantifies 2D velocity components as the plume moves through space in both an Eulerian and a Lagrangian framework. We apply this algorithm to three data sets: a synthetic data set and two laboratory experiments. Performance of this algorithm is reported by the mean (bias error, B) and standard deviation (random error, SD) of the residuals between its results and the reference data. For the synthetic data, the algorithm produces maximum errors of B & SD = 32% & 23% in the Eulerian framework, respectively, and B & SD = −0.04% & 3.9% in the Lagrangian framework. The small-scale laboratory experimental data requires the Eulerian framework and produce errors of B & SD = −0.5% & 33%. The Lagrangian framework is used on the large-scale laboratory experimental data and produces errors of B & SD = 5% & 44%. Mapping the porous media architecture shows negligible error for reconstructing calibration grains of known dimensions. Article DOI: 10.1088/1361-6501/acfb2b

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