A Method for Deducing the Self-Diffusion Coefficient of a Single Analog Molecule within a Liquid-State Flow

Philip Troy Brown; Russell G. Keanini; Jerry Lynn Dahlberg; Peter Thomas Tkacik

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Military and Veterans Division (MVD) Technical Session 2
Tagged Division: Military and Veterans Division (MVD)
Page Count: 6
DOI: 10.18260/1-2--42427
Permanent URL: https://peer.asee.org/42427
Download Count: 96

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Paper Authors

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Philip Troy Brown University of North Carolina, Charlotte

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Philip Brown is a mechanical engineer conducting research in the field of molecular hydrodynamics at the University of North Carolina at Charlotte, where he also serves as the President of the Society of Student Veterans in Engineering (SSVE) and the assistant program manager for the Shaping Experimental Research for Veteran Education (SERVE) STEM engagement program. Prior to beginning this research, he spent 6 years in the United States Navy working as a submarine missile technician specializing in nuclear weapons. He spends his free time at racetracks, working with a stockcar racing organization that uses racing as a medium to promote the POW-MIA awareness campaign.

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Russell G. Keanini University of North Carolina, Charlotte

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Jerry Lynn Dahlberg University of Tennessee Space Institute orcid.org/0000-0003-2778-5349

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Jerry Dahlberg is the Associate Director for Aerospace and Defense at the University of Tennessee Space Institute. Prior to joining UTSI, he was an Assistant Teaching Professor and Senior Design Committee Chair at the University of North Carolina at Charlotte. He received a B.S. degree in Mechanical Engineering Science in 2014, M.S. in Mechanical Engineering in 2016 and PhD in Mechanical Engineering in 2018 from the University of North Carolina at Charlotte.

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Peter Thomas Tkacik University of North Carolina, Charlotte

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Peter Tkacik is a retired Professor of mechanical engineering. His area of research is in the engagement of students through hands-on learning research activities.

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Abstract

This work was conducted as part of the Shaping Experiential Research for Veteran Education (SERVE) program for undergraduate students. This program aims to engage veterans in engineering and STEM related topics that address US Navy research priorities by: a) increasing the number of veterans obtaining graduate STEM degrees, and b) providing these students with hands-on research experience, working alongside experienced faculty and graduate students. Research conducted at UNC Charlotte, and funded by the Office of Naval Research, has demonstrated the viability of using vibrating grain beds as macroscopic analogs for studying dense, liquid-state molecular hydrodynamic flows. Unlike other molecular hydrodynamic methods, vibrating grain beds allow direct observation of particle interactions in liquid flows. Previous experiments using this method have concentrated on observing the molecular interactions of particles in an entire flow-field. However, the present inquiry concentrates on tracking the random displacements of a single grain, i.e., an analog atom or molecule, as it undergoes simultaneous transport by deterministic bulk fluid motion and random, thermally driven self-diffusive hops. The study objective centers on using these measurements to estimate the effective self-diffusion coefficient of a single, molecule-like grain within a vibrated granular fluid. Experimentally, this single grain is first heated in a convection oven and then introduced into a granular flow field having a lower, spatially uniform ambient temperature. The grain is then tracked with a thermal imaging camera, allowing direct observation of the grain’s random path within the flow. The experiment is performed multiple times, with each realization processed into individual, digitized paths using PIV (Particle Image Velocimetry) software. The set of experimentally observed paths is then averaged, creating a mean particle path. To extract the self-diffusion coefficient, individual grain paths will be modeled as single realizations of a stochastic Weiner process. Finally, using the estimated self-diffusion coefficient, the effective grain fluid viscosity will be determined using the Stokes-Einstein relation.

Citation
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Brown, P. T., & Keanini, R. G., & Dahlberg, J. L., & Tkacik, P. T. (2023, June), A Method for Deducing the Self-Diffusion Coefficient of a Single Analog Molecule within a Liquid-State Flow Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42427

TY - CPAPER
AB - This work was conducted as part of the Shaping Experiential Research for Veteran Education (SERVE) program for undergraduate students. This program aims to engage veterans in engineering and STEM related topics that address US Navy research priorities by: a) increasing the number of veterans obtaining graduate STEM degrees, and b) providing these students with hands-on research experience, working alongside experienced faculty and graduate students.
Research conducted at UNC Charlotte, and funded by the Office of Naval Research, has demonstrated the viability of using vibrating grain beds as macroscopic analogs for studying dense, liquid-state molecular hydrodynamic flows. Unlike other molecular hydrodynamic methods, vibrating grain beds allow direct observation of particle interactions in liquid flows. Previous experiments using this method have concentrated on observing the molecular interactions of particles in an entire flow-field. However, the present inquiry concentrates on tracking the random displacements of a single grain, i.e., an analog atom or molecule, as it undergoes simultaneous transport by deterministic bulk fluid motion and random, thermally driven self-diffusive hops. The study objective centers on using these measurements to estimate the effective self-diffusion coefficient of a single, molecule-like grain within a vibrated granular fluid. Experimentally, this single grain is first heated in a convection oven and then introduced into a granular flow field having a lower, spatially uniform ambient temperature. The grain is then tracked with a thermal imaging camera, allowing direct observation of the grain’s random path within the flow. The experiment is performed multiple times, with each realization processed into individual, digitized paths using PIV (Particle Image Velocimetry) software. The set of experimentally observed paths is then averaged, creating a mean particle path. To extract the self-diffusion coefficient, individual grain paths will be modeled as single realizations of a stochastic Weiner process. Finally, using the estimated self-diffusion coefficient, the effective grain fluid viscosity will be determined using the Stokes-Einstein relation.

AU - Philip Troy Brown
AU - Russell G. Keanini
AU - Jerry Lynn Dahlberg
AU - Peter Thomas Tkacik
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - A Method for Deducing the Self-Diffusion Coefficient of a Single Analog Molecule within a Liquid-State Flow
UR - https://peer.asee.org/42427
DO - 10.18260/1-2--42427
ER -