Statistics of particle diffusion subject to oscillatory flow in a porous bed

[Display omitted] •Oscillating flow enhances particle diffusion in a porous media.•Diffusion results from oscillation plus random hindering.•Experiments exhibit statistical measures similar to random walk diffusion.•Particle hold-up duration exhibits log-normal distribution.•Stochastic model display...

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Veröffentlicht in:Chemical engineering science Jg. 231; S. 116239
Hauptverfasser: Marshall, Jeffrey S., Arnold, Chloe, Curran, Kelly, Chivers, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.02.2021
Schlagworte:
Hindered diffusion
Oscillatory diffusion
Particle diffusion
Porous bed
Oscillatory diffusion
Hindered diffusion
Particle diffusion
Porous bed
ISSN:0009-2509, 1873-4405
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Zusammenfassung:[Display omitted] •Oscillating flow enhances particle diffusion in a porous media.•Diffusion results from oscillation plus random hindering.•Experiments exhibit statistical measures similar to random walk diffusion.•Particle hold-up duration exhibits log-normal distribution.•Stochastic model displays similar characteristics as experiments. An experimental study was conducted of particle diffusion within a porous bed when exposed to oscillatory flow. The particle oscillates up and down within the porous bed in response to the oscillatory flow, but also becomes intermittently stuck for time intervals of varying duration. The combination of oscillating flow and random hindering of the particle motion by the porous bed leads to a diffusive process called oscillatory diffusion. A variety of statistical measures are used to characterize the particle diffusion under the oscillatory flow. These measures show that the experimental data exhibit characteristics of both classical diffusive processes as well as oscillatory processes. The particle hold-up time duration was found to be well fit by a log-normal distribution for all experimental cases examined. A simple stochastic model that captures the key features of the oscillatory diffusion process is shown to yield statistical measures that compare well with experimental data.
ISSN:0009-2509
1873-4405
DOI:10.1016/j.ces.2020.116239