Transmission probability of diffusing particles—A case study

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A diffusing particle is subjected to a variety of collisions that lead to a random or Brownian motion. The aim of this study is to compute the transmission probability and highlight the visualization of Brownian motion under the conditions of varying viscosity, particle size and temperature. As a preliminary study, two simulation results such as transport of diluted species and particle based approach have been compared and the transmission probability is computed by a particle based approach. We validate our results with Stokes-Einstein equation and Fang and Ning’s experimental and theoretical work and show that the transmission probability increases with decrease in viscosity and particle size and increase in temperature. The obtained results also describe the Brownian motion for various particle sizes, viscosity and temperature.

Original languageEnglish (US)
Title of host publicationTMS 2017 146th Annual Meeting
PublisherSpringer International Publishing
Pages747-757
Number of pages11
ISBN (Print)9783319514925
DOIs
StatePublished - 2017
Event146th Annual Meeting and Exhibition Supplemental, TMS 2017 - San Diego, United States
Duration: Feb 26 2017Mar 2 2017

Publication series

NameMinerals, Metals and Materials Series
VolumePart F6
ISSN (Print)2367-1181
ISSN (Electronic)2367-1696

Other

Other146th Annual Meeting and Exhibition Supplemental, TMS 2017
Country/TerritoryUnited States
CitySan Diego
Period2/26/173/2/17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Energy Engineering and Power Technology
  • Mechanics of Materials
  • Metals and Alloys
  • Materials Chemistry

Keywords

  • Agglomeration
  • Brownian motion
  • Diffusion
  • Particles

Fingerprint

Dive into the research topics of 'Transmission probability of diffusing particles—A case study'. Together they form a unique fingerprint.

Cite this