Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach

S. Mohammad Nejad; S.V. Nedea; Arjan J.H. Frijns

Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach

S. Mohammad Nejad, S.V. Nedea, Arjan J.H. Frijns

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

In rarefied gas flows, the non-continuum effects, such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium, scattering models with a limited number of parameters cannot completely capture the reflection of gas molecules at the solid boundary. In this work, the Gaussian Mixture (GM) approach, an unsupervised machine learning technique, is employed to construct a statistical gas-surface scattering model. The main input required to train the GM model are the MD collisional data. In this paper we consider two cases: a monoatomic (Ar-Au) and a diatomic (H2-Ni) gas-wall interaction.

Original language	English
Title of host publication	Proceedings of the 4th European Conference on Non-Equilibrium Gas Flows
Editors	Stéphane Colin, Arjan Frijns, Dimitris Valougeorgis
Place of Publication	Eindhoven
Publisher	Eindhoven University of Technology
Pages	107-111
Number of pages	5
Publication status	Published - 29 Mar 2023
Event	4th European Conference on Non-Equilibrium Gas Flows, NEGF23 - TU Eindhoven, Eindhoven, Netherlands Duration: 29 Mar 2023 → 31 Mar 2023 Conference number: 4 https://negf23.sciencesconf.org/

Conference

Conference	4th European Conference on Non-Equilibrium Gas Flows, NEGF23
Abbreviated title	NEGF23
Country/Territory	Netherlands
City	Eindhoven
Period	29/03/23 → 31/03/23
Internet address	https://negf23.sciencesconf.org/

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NEGF23_434853Final published version, 1.42 MB

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Mohammad Nejad, S. ; Nedea, S.V. ; Frijns, Arjan J.H. / Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach. Proceedings of the 4th European Conference on Non-Equilibrium Gas Flows. editor / Stéphane Colin ; Arjan Frijns ; Dimitris Valougeorgis. Eindhoven : Eindhoven University of Technology, 2023. pp. 107-111

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title = "Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach",

abstract = "In rarefied gas flows, the non-continuum effects, such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium, scattering models with a limited number of parameters cannot completely capture the reflection of gas molecules at the solid boundary. In this work, the Gaussian Mixture (GM) approach, an unsupervised machine learning technique, is employed to construct a statistical gas-surface scattering model. The main input required to train the GM model are the MD collisional data. In this paper we consider two cases: a monoatomic (Ar-Au) and a diatomic (H2-Ni) gas-wall interaction.",

author = "{Mohammad Nejad}, S. and S.V. Nedea and Frijns, {Arjan J.H.}",

year = "2023",

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pages = "107--111",

editor = "St{\'e}phane Colin and Arjan Frijns and Dimitris Valougeorgis",

booktitle = "Proceedings of the 4th European Conference on Non-Equilibrium Gas Flows",

publisher = "Eindhoven University of Technology",

note = "4th European Conference on Non-Equilibrium Gas Flows, NEGF23, NEGF23 ; Conference date: 29-03-2023 Through 31-03-2023",

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Mohammad Nejad, S , Nedea, SV & Frijns, AJH 2023, Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach. in S Colin, A Frijns & D Valougeorgis (eds), Proceedings of the 4th European Conference on Non-Equilibrium Gas Flows., NEGF23-434853, Eindhoven University of Technology, Eindhoven, pp. 107-111, 4th European Conference on Non-Equilibrium Gas Flows, NEGF23, Eindhoven, Netherlands, 29/03/23.

Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach. / Mohammad Nejad, S.; Nedea, S.V.; Frijns, Arjan J.H.
Proceedings of the 4th European Conference on Non-Equilibrium Gas Flows. ed. / Stéphane Colin; Arjan Frijns; Dimitris Valougeorgis. Eindhoven: Eindhoven University of Technology, 2023. p. 107-111 NEGF23-434853.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach

AU - Mohammad Nejad, S.

AU - Nedea, S.V.

AU - Frijns, Arjan J.H.

N1 - Conference code: 4

PY - 2023/3/29

Y1 - 2023/3/29

N2 - In rarefied gas flows, the non-continuum effects, such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium, scattering models with a limited number of parameters cannot completely capture the reflection of gas molecules at the solid boundary. In this work, the Gaussian Mixture (GM) approach, an unsupervised machine learning technique, is employed to construct a statistical gas-surface scattering model. The main input required to train the GM model are the MD collisional data. In this paper we consider two cases: a monoatomic (Ar-Au) and a diatomic (H2-Ni) gas-wall interaction.

AB - In rarefied gas flows, the non-continuum effects, such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium, scattering models with a limited number of parameters cannot completely capture the reflection of gas molecules at the solid boundary. In this work, the Gaussian Mixture (GM) approach, an unsupervised machine learning technique, is employed to construct a statistical gas-surface scattering model. The main input required to train the GM model are the MD collisional data. In this paper we consider two cases: a monoatomic (Ar-Au) and a diatomic (H2-Ni) gas-wall interaction.

M3 - Conference contribution

SP - 107

EP - 111

BT - Proceedings of the 4th European Conference on Non-Equilibrium Gas Flows

A2 - Colin, Stéphane

A2 - Frijns, Arjan

A2 - Valougeorgis, Dimitris

PB - Eindhoven University of Technology

CY - Eindhoven

T2 - 4th European Conference on Non-Equilibrium Gas Flows, NEGF23

Y2 - 29 March 2023 through 31 March 2023

ER -

Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach

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