A Gas-Surface Interaction Model for Diatomic Rarefied Gas Flows Based on an Unsupervised Machine Learning Technique

Shahin Mohammad Nejad, E. Iype, Frank A. Peters, A.L.B. Vollebregt, Silvia V. Gaastra-Nedea, Arjan J.H. Frijns

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In this work, the Gaussian Mixture (GM) model, which is an unsupervised machine learning technique, is utilized to construct a statistical gas-solid surface scattering model based on the collisional data obtained from Molecular Dynamics (MD) simulations. The main advantage of the scattering kernel constructed based on the GM model over the existing stochastic empirical scattering kernels is that its capability is not restricted by the finite number of adjustable parameters, which are required to be known in advance.
The GM model is used to study Couette flow for Hydrogen gas confined between two parallel infinite Nickel walls at different temperatures: at the bottom wall T_b=300K; at the top wall T_t = 500K . In order to model Couette flow condition, walls have been moving with velocity 𝑢_𝑤 = 0. 5 √ (2 𝑘 𝑇_𝑏/𝑚).
The results obtained from the GM model and the Cercignani-Lampis-Lord (CLL) scattering kernel are compared against the MD collisional data. Both the distribution of the predicted post-collisional velocities, and accommodation coefficients of the GM kernel are in good agreement with MD and show better agreement than the CLL kernel.
Original languageEnglish
Title of host publicationPre-RGD32 Online Workshop on Recent Hot Topics in Rarefied Gas Dynamics
EditorsRho Shin Myong, Domenico Bruno, Kun Xu, Jong-Shinn Wu
Pages92
Publication statusPublished - 7 Jul 2021
EventPre-RGD32 Online Workshop on Recent Hot Topics in Rarefied Gas Dynamics - http://www.rgd32.org/preworkshop.asp
Duration: 7 Jul 202110 Jul 2021

Conference

ConferencePre-RGD32 Online Workshop on Recent Hot Topics in Rarefied Gas Dynamics
Period7/07/2110/07/21

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