Numerical methodologies for investigation of moderate-velocity flow using a hybrid computational fluid dynamics — molecular dynamics simulation approach

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...

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Vydané v:Journal of mechanical science and technology Ročník 28; číslo 1; s. 245 - 253
Hlavní autori: Ko, Soon-Heum, Kim, Nayong, Jha, Shantenu, Nikitopoulos, Dimitris E., Moldovan, Dorel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.01.2014
Springer Nature B.V
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ISSN:1738-494X, 1976-3824, 1976-3824
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Abstract 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.
AbstractList 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.
Numerical approaches are presented to minimize the statistical errors inherently present due to finite sampling and the presence ofthermal 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 ofsignificant molecular-level phenomena, than the traditional continuum-based simulation techniques. It also involves less computationalcost than the pure particle-based MD. Despite these advantages the hybrid CFD-MD methodology has been applied mostly in flow studiesat high velocities, mainly because of the higher statistical errors associated with low velocities. As an alternative to the costly increaseof the size of the MD region to decrease statistical errors, we investigate a few numerical approaches that reduce sampling noise of thesolution at moderate-velocities. These methods are based on sampling of multiple simulation replicas and linear regression of multiplespatial/temporal samples. We discuss the advantages and disadvantages of each technique in the perspective of solution accuracy andcomputational cost. KCI Citation Count: 1
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.[PUBLICATION ABSTRACT]
Author Nikitopoulos, Dimitris E.
Ko, Soon-Heum
Kim, Nayong
Moldovan, Dorel
Jha, Shantenu
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  givenname: Dorel
  surname: Moldovan
  fullname: Moldovan, Dorel
  organization: Department of Mechanical and Industrial Engineering, Louisiana State University
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Cites_doi 10.1017/S0022112003007225
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Issue 1
Keywords Hybrid CFD-MD approach
Temporal regression
Nanofluidics
Molecular statistical errors (molecular sampling noise)
Replica sampling
Spatial regression
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SubjectTerms Computational efficiency
Computational fluid dynamics
Computer simulation
Control
Dynamical Systems
Engineering
Error analysis
Industrial and Production Engineering
Mathematical models
Mechanical Engineering
MEDICIN
MEDICINE
Molecular dynamics
Sampling
Statistical methods
Vibration
기계공학
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Volume 28
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