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Numerical methodologies for investigation of moderate-velocity flow using a hybrid computational fluid dynamics - molecular dynamics simulation approach
Linkoping University, National Supercomper Center, Sweden.
Louisiana State University, LA 70803 USA .
Rutgers State University, NJ 08854 USA .
Louisiana State University, LA 70803 USA .
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2014 (English)In: Journal of Mechanical Science and Technology, ISSN 1738-494X, E-ISSN 1976-3824, Vol. 28, no 1, 245-253 p.Article in journal (Refereed) Published
Abstract [en]

Numerical approaches are presented to minimize the statistical errors inherently present due to finite sampling and the presence of thermal fluctuations in the molecular region of a hybrid computational fluid dynamics (CFD) - molecular dynamics (MD) flow solution. Near the fluid-solid interface the hybrid CFD-MD simulation approach provides a more accurate solution, especially in the presence of significant molecular-level phenomena, than the traditional continuum-based simulation techniques. It also involves less computational cost than the pure particle-based MD. Despite these advantages the hybrid CFD-MD methodology has been applied mostly in flow studies at high velocities, mainly because of the higher statistical errors associated with low velocities. As an alternative to the costly increase of the size of the MD region to decrease statistical errors, we investigate a few numerical approaches that reduce sampling noise of the solution at moderate-velocities. These methods are based on sampling of multiple simulation replicas and linear regression of multiple spatial/temporal samples. We discuss the advantages and disadvantages of each technique in the perspective of solution accuracy and computational cost.

Place, publisher, year, edition, pages
Springer Verlag (Germany) , 2014. Vol. 28, no 1, 245-253 p.
Keyword [en]
Hybrid CFD-MD approach; Nanofluidics; Replica sampling; Spatial regression; Molecular statistical errors (molecular sampling noise); Temporal regression
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-105588DOI: 10.1007/s12206-013-0962-5ISI: 000331765000025OAI: oai:DiVA.org:liu-105588DiVA: diva2:708720
Available from: 2014-03-28 Created: 2014-03-27 Last updated: 2017-12-05

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf