Gaussian mixture models for diatomic gas−surface interactions under thermal non-equilibrium conditions

Wu, Hui; Chen, Weifang; Jiang, Zhongzheng

doi:10.1063/5.0099863

Cited by 10 publications

(1 citation statement)

References 67 publications

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“…Nevertheless, in all these scattering kernels, the gas-gas interactions that can affect the reflected gas molecules properties in the early transition regime (0.1¡Kn¡1) are ignored, and these wall models cannot deal with adsorption-related problems. Machine learning is another promising technique that can be used to establish a gas scattering kernel directly based on the collisional data obtained from MD simulations [28][29][30][31][32]. As an example, in our previous works [29,31], the Gaussian mixture (GM) approach, an unsupervised machine learning approach, was employed to construct a scattering kernel for monoatomic gases (Ar, He) interacting with the Au surface and diatomic gases (H 2 , N 2 ) interacting with the Ni surface, respectively.…”

Section: Introductionmentioning

confidence: 99%

A hybrid Gaussian Mixture/DSMC approach to study the Fourier thermal problem

Nejad,

Peters,

Nedea

et al. 2023

Preprint

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In rarefied gas dynamics scattering kernels deserve special attention since they contain all the essential information about the effects of physical and chemical properties of the gas-solid surface interface on the gas scattering process. However, to study the impact of the gas-surface interactions on the large-scale behavior of fluid flows, these scattering kernels need to be integrated in larger-scale models like Direct Simulation Monte Carlo (DSMC). In this work, the Gaussian mixture (GM) model, an unsupervised machine learning approach, is utilized to establish a scattering kernel for monoatomic (Ar) and diatomic (H\textsubscript{2}) gases directly from Molecular Dynamics (MD) simulations data. The GM scattering kernel is coupled to a pure DSMC solver to study isothermal and non-isothermal rarefied gas flows in a system with two parallel walls. To fully examine the coupling mechanism between the GM scattering kernel and the DSMC approach, a one-to-one correspondence between MD and DSMC particles is considered here. Benchmarked by MD results, the performance of the GM-DSMC is assessed against the Cercignani-Lampis-Lord (CLL) kernel incorporated into DSMC simulation (CLL-DSMC). The comparison of various physical and stochastic parameters shows the better performance of the GM-DSMC approach. Especially for the diatomic system, the GM-DSMC outperforms the CLL-DSMC approach. The fundamental superiority of the GM-DSMC approach confirms its potential as a multi-scale simulation approach for accurately measuring flow field properties in systems with highly nonequilibrium conditions.

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Section: Introductionmentioning

confidence: 99%

A hybrid Gaussian Mixture/DSMC approach to study the Fourier thermal problem

Nejad,

Peters,

Nedea

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

A hybrid Gaussian mixture/DSMC approach to study the Fourier thermal problem

Mohammad Nejad,

Peters,

Nedea

et al. 2024

Microfluid Nanofluid

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In rarefied gas dynamics scattering kernels deserve special attention since they contain all the essential information about the effects of physical and chemical properties of the gas–solid surface interface on the gas scattering process. However, to study the impact of the gas–surface interactions on the large-scale behavior of fluid flows, these scattering kernels need to be integrated in larger-scale models like Direct Simulation Monte Carlo (DSMC). In this work, the Gaussian mixture (GM) model, an unsupervised machine learning approach, is utilized to establish a scattering kernel for monoatomic (Ar) and diatomic ($$\hbox {H}_{2}$$ H 2 ) gases directly from Molecular Dynamics (MD) simulations data. The GM scattering kernel is coupled to a pure DSMC solver to study isothermal and non-isothermal rarefied gas flows in a system with two parallel walls. To fully examine the coupling mechanism between the GM scattering kernel and the DSMC approach, a one-to-one correspondence between MD and DSMC particles is considered here. Benchmarked by MD results, the performance of the GM-DSMC is assessed against the Cercignani–Lampis–Lord (CLL) kernel incorporated into DSMC simulation (CLL-DSMC). The comparison of various physical and stochastic parameters shows the better performance of the GM-DSMC approach. Especially for the diatomic system, the GM-DSMC outperforms the CLL-DSMC approach. The fundamental superiority of the GM-DSMC approach confirms its potential as a multi-scale simulation approach for accurately measuring flow field properties in systems with highly nonequilibrium conditions.

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Gas-surface interaction features under effects of gas-gas molecules interaction in high-speed flows

TAO,

WANG

2024

Chinese Journal of Aeronautics

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Gaussian mixture models for diatomic gas−surface interactions under thermal non-equilibrium conditions

Cited by 10 publications

References 67 publications

A hybrid Gaussian Mixture/DSMC approach to study the Fourier thermal problem

A hybrid Gaussian Mixture/DSMC approach to study the Fourier thermal problem

A hybrid Gaussian mixture/DSMC approach to study the Fourier thermal problem

Gas-surface interaction features under effects of gas-gas molecules interaction in high-speed flows

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