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

Shahin Mohammad Nejad1, Eldhose Iype2, Silvia Nedea1

  • 1Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.

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The Gaussian Mixture (GM) model, a machine learning technique, accurately predicts gas molecule behavior at surfaces, outperforming traditional models in rarefied gas dynamics simulations.

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Area of Science:

  • Fluid Dynamics
  • Statistical Mechanics
  • Machine Learning

Background:

  • Rarefied gas flows exhibit boundary phenomena like velocity slip and temperature jump.
  • Existing boundary models struggle with complex gas-solid interactions and local nonequilibrium states.
  • Accurate modeling of gas-surface interactions is crucial for understanding complex flow conditions.

Purpose of the Study:

  • To develop a novel statistical gas-solid surface scattering model using machine learning.
  • To investigate the performance of the Gaussian Mixture (GM) model against established models.
  • To assess the GM model's capability in simulating rarefied gas flows with complex boundary conditions.

Main Methods:

  • Employed unsupervised machine learning (Gaussian Mixture model) to analyze molecular dynamics (MD) simulation data.
  • Developed a statistical scattering model based on MD-derived collisional data.
  • Simulated Couette flow for Argon (Ar) and Helium (He) gases between gold walls.

Main Results:

  • The GM model demonstrated excellent agreement with MD simulation data for postcollisional velocity distributions.
  • GM model significantly outperformed the Cercignani-Lampis-Lord (CLL) scattering kernel in predicting accommodation coefficients.
  • For Helium, the GM model's energy accommodation coefficient closely matched MD results, unlike the CLL model.

Conclusions:

  • The Gaussian Mixture model provides a highly accurate statistical representation of gas-surface scattering.
  • The GM model shows significant potential for developing generalized boundary conditions in complex, nonequilibrium rarefied gas flows.
  • This machine learning approach offers a superior alternative to traditional scattering kernels for detailed gas-surface interaction modeling.