Establishing a data-based scattering kernel model for gas–solid interaction by molecular dynamics simulation
DOI10.1017/jfm.2021.828zbMath1495.76096OpenAlexW3206658808MaRDI QIDQ5158476
Chengqian Song, Zijing Wang, Xisheng Luo, Feng-Hua Qin
Publication date: 25 October 2021
Published in: Journal of Fluid Mechanics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1017/jfm.2021.828
kinetic theorymachine learningrarefied gas flowCercignani-Lampis-Lord modelmolecule incidence velocitymolecule reflection velocityvariable accommodation coefficient
Learning and adaptive systems in artificial intelligence (68T05) Multiphase and multicomponent flows (76T99) Rarefied gas flows, Boltzmann equation in fluid mechanics (76P05) Basic methods in fluid mechanics (76M99)
Cites Work
- Unnamed Item
- Atomistic hybrid DSMC/NEMD method for nonequilibrium multiscale simulations
- Mass flow and tangential momentum accommodation in silicon micromachined channels
- An analytically predictive model for moderately rarefied gas flow
- Multistage gas–surface interaction model for the direct simulation Monte Carlo method
- Second-order slip laws in microchannels for helium and nitrogen
- Some further extensions of the Cercignani–Lampis gas–surface interaction model
- Kinetic models for gas-surface interactions
- Drag and thermophoresis on a sphere in a rarefied gas based on the Cercignani–Lampis model of gas–surface interaction
- Scattering properties and scattering kernel based on the molecular dynamics analysis of gas-wall interaction
- A high-order moment approach for capturing non-equilibrium phenomena in the transition regime
- Mass flow rate measurements in a microchannel, from hydrodynamic to near free molecular regimes
- Basic Concepts in Computational Physics
This page was built for publication: Establishing a data-based scattering kernel model for gas–solid interaction by molecular dynamics simulation