A coarse grained transport model for nanofluidic systems

Ramlawi, Nabil

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https://hdl.handle.net/2142/102433 Copy

Description

Title

A coarse grained transport model for nanofluidic systems

Author(s)

Ramlawi, Nabil

Issue Date

2018-11-26

Director of Research (if dissertation) or Advisor (if thesis)

Aluru, Narayana

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Molecular Dynamics
• Nanofluidic Transport
• Coarse-graining

Abstract

Molecular Dynamics (MD) is an important tool to simulate flows at the nanoscale. The limitation of MD in simulating important biological and chemical systems having a large length and time scale, increased the interest in efficient coarse-grained (CG) models. Although many existing CG models for various fluids are able to capture structure and dynamics of the bulk fluid accurately, these models are not suited to describe transport phenomena involving explicit walls in nano-channels. Previous coarse-grained models for confined fluids are only optimized to match the structure of the confined fluid. Here we introduce a complete CG transport model for a single component fluid in nano-channels having explicit walls. The model, which was applied to the water-graphene system, was able to demonstrate a very good match, with the structure (error< 7%) and dynamical (error<1%) equilibrium properties of MD simulations. Moreover, the CG model was able to reproduce the MD results for water transport in a Poiseuille flow configuration with an error < 5%. The accuracy of the model was transferable through different configurations and forcing conditions up to a critical force, where the MD slip velocity starts to deviate from the equilibrium prediction. Finally, the CG model was able to achieve ≈ 20x speedup compared to MD simulations, making it more suitable for flows close to experimental conditions, where MD produces a poor signal to noise ratio.

Graduation Semester

2018-12

Type of Resource

text

Permalink

http://hdl.handle.net/2142/102433

Copyright and License Information

Copyright 2018 Nabil Ramlawi

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