Prediction of thermal conductance and friction coefficients at solid-gas interface from statistical learning of collisions

Abstract : In this paper, we present the construction of statistical models of gas-wall collision based on data issued from Molecular Dynamics (MD) simulations and use them to predict the velocity slip and temperature jump coefficients at the gas-solid interface. The Gaussian Mixture (GM) model, an unsupervised learning technique, is chosen for this purpose. The model shares some similarities with the well-known Cercignani-Lampis model in kinetic theory but it is more robust due to the unlimited number of Gaussian functions used and the ability to deal with correlated data of high dimensions. Applications to real gas-wall systems (Argon-Gold and Helium-Gold) confirm the good performance of the model. The trained GM model predicts physical and statistical properties including accommodation, friction and thermal conductance coefficients in excellent agreement with the MD model.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01873261
Contributor : Quy Dong To <>
Submitted on : Thursday, September 13, 2018 - 9:44:48 AM
Last modification on : Wednesday, March 27, 2019 - 1:35:03 AM
Long-term archiving on : Friday, December 14, 2018 - 1:01:13 PM

File

main.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01873261, version 1

Citation

Meng Liao, Quy-Dong To, Céline Léonard, Wenlu Yang. Prediction of thermal conductance and friction coefficients at solid-gas interface from statistical learning of collisions. Physical Review E , American Physical Society (APS), 2018. ⟨hal-01873261⟩

Share

Metrics

Record views

60

Files downloads

94