An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material

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@Article{Garg:2014:SMPT,
  author =       "A. Garg and V. Vijayaraghavan and C. H. Wong and 
                 K. Tai and Liang Gao",
  title =        "An embedded simulation approach for modeling the
                 thermal conductivity of {2D} nanoscale material",
  journal =      "Simulation Modelling Practice and Theory",
  year =         "2014",
  volume =       "44",
  month =        may,
  pages =        "1--13",
  ISSN =         "1569-190X",
  DOI =          "doi:10.1016/j.simpat.2014.02.003",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1569190X14000276",
  keywords =     "genetic algorithms, genetic programming, multi-gene
                 genetic programming, Graphene modelling, Nanomaterial
                 characteristics, Nanomaterial modelling, Thermal
                 conductivity modelling",
  abstract =     "The thermal property of single layer graphene sheet is
                 investigated in this work by using an embedded approach
                 of molecular dynamics (MD) and soft computing method.
                 The effect of temperature and Stone-Thrower-Wales (STW)
                 defects on the thermal conductivity of graphene sheet
                 is first analysed using MD simulation. The data
                 obtained using the MD simulation is then fed into the
                 paradigm of soft computing approach, multi-gene genetic
                 programming (MGGP), which was specifically designed to
                 model the response of thermal conductivity of graphene
                 sheet with changes in system temperature and STW defect
                 concentration. We find that our proposed MGGP model is
                 able to model the thermal conductivity of graphene
                 sheet very well which can be used to complement the
                 analytical solution developed by MD simulation.
                 Additionally, we also conducted sensitivity and
                 parametric analysis to find out specific influence and
                 variation of each of the input system parameters on the
                 thermal conductivity of graphene sheet. It was found
                 that the STW defects has the most dominating influence
                 on the thermal conductivity of graphene sheet.",
}

Genetic Programming entries for Akhil Garg Venkatesh Vijayaraghavan Chee How Wong Kang Tai Liang Gao

Citations