2023 ASEE Annual Conference & Exposition

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.