Molecular dynamics simulation of particle trajectory for the evaluation of surface accommodation coefficients

Peddakotla, Sai Abhishek; Kammara, Kishore K.; Kumar, Rakesh

doi:10.1007/s10404-019-2248-4

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…In this model, a fixed empirical correlation is imposed between α t and the tangential energy accommodation coefficient α TE , that is, α TE = α t (2 − α t ). To generate new velocities after collision according to the CLL scattering kernel the algorithm provided in the paper by Peddakotla et al [42] is employed, in which including a separate AC for each spatial direction assists the comparison with MD simulation results.…”

Section: B Cll Scattering Kernelmentioning

confidence: 99%

Modeling rarefied gas-solid surface interactions for Couette flow with different wall temperatures using an unsupervised machine learning technique

et al. 2021

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Section: B Cll Scattering Kernelmentioning

confidence: 99%

Modeling rarefied gas-solid surface interactions for Couette flow with different wall temperatures using an unsupervised machine learning technique

et al. 2021

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“…Previous studies of the gas–solid interaction were always embedded in the investigation of gas flow (Peddakotla, Kammara & Kumar 2019), which was a natural choice since the interaction was tightly coupled to the flow. However, to reduce the computation cost of the MD method, the simulation of the gas–solid interaction is always restricted to a very small computation domain, or performed for special conditions of gas flow.…”

Section: Introductionmentioning

confidence: 99%

Establishing a data-based scattering kernel model for gas–solid interaction by molecular dynamics simulation

et al. 2021

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Scattering kernel models for gas–solid interaction are crucial for rarefied gas flows and microscale flows. However, most existing models depend on certain accommodation coefficients (ACs). We propose here to construct a data-based model using molecular dynamics (MD) simulation and machine learning. The gas–solid interaction is first modelled by 100 000 MD simulations of a single gas molecule reflecting on the wall surface, which is fulfilled by GPU parallel technology. The results showed a correlation of the reflection velocity with the incidence velocity in the same direction, and also revealed correlations that may exist in different directions, which are neglected by the traditional gas–solid interaction model. Inspired by the sophisticated Cercignani–Lampis–Lord (CLL) model, two improved scattering kernels were constructed to better reproduce the probability density of velocity determined from MD simulation. The first one adopts variable ACs which depend on the incidence velocity and the second one combines three CLL-like kernels. All the parameters in the improved kernels are automatically chosen by the machine learning method. Compared with the numerical experiments of a molecular beam, the reconstructed scattering kernels are basically consistent with the MD results.

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Development of empirical relationships for surface accommodation coefficients through investigation of nano-poiseuille flows using molecular dynamics method

Kammara

Kumar

2020

Microfluid Nanofluid

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Molecular dynamics simulation of particle trajectory for the evaluation of surface accommodation coefficients

Cited by 11 publications

References 29 publications

Modeling rarefied gas-solid surface interactions for Couette flow with different wall temperatures using an unsupervised machine learning technique

Modeling rarefied gas-solid surface interactions for Couette flow with different wall temperatures using an unsupervised machine learning technique

Establishing a data-based scattering kernel model for gas–solid interaction by molecular dynamics simulation

Development of empirical relationships for surface accommodation coefficients through investigation of nano-poiseuille flows using molecular dynamics method

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