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

Nejad, Shahin Mohammad; Iype, Eldhose; Nedea, S.V.; Frijns, Ajh Arjan; Smeulders, David

doi:10.1103/physreve.104.015309

Cited by 11 publications

(3 citation statements)

References 42 publications

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“…Using these numbers of the Gaussian functions, the training of the GM model on a regular laptop computer takes around 3 and 40 minutes for the Ar-Au and H 2 -Ni systems. The GM model manifests its best performance when all the components of the training data are normally distributed [29]. In the case of both gas-solid pairs considered in this work, except for the normal velocities (v ′ y ,v y ) following the Rayleigh distributions, the other components in the training data follow a Gaussian distribution.…”

Section: Gm Scattering Modelmentioning

confidence: 96%

“…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%

“…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. In these studies, a two parallel walls MD setup was used as the reference system to include the impact of both gas-wall and gas-gas interactions on the postcollisional behavior of gas molecules.…”

Section: Introductionmentioning

confidence: 99%

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

Nejad,

Peters,

Nedea

et al. 2023

Preprint

Self Cite

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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: Gm Scattering Modelmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nejad,

Peters,

Nedea

et al. 2023

Preprint

Self Cite

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Computation of flow rates in rarefied gas flow through circular tubes via machine learning techniques

Sofos,

Dritselis,

Misdanitis

et al. 2023

Microfluid Nanofluid

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Kinetic theory and modeling have been proven extremely suitable in computing the flow rates in rarefied gas pipe flows, but they are computationally expensive and more importantly not practical in design and optimization of micro- and vacuum systems. In an effort to reduce the computational cost and improve accessibility when dealing with such systems, two efficient methods are employed by leveraging machine learning (ML). More specifically, random forest regression (RFR) and symbolic regression (SR) have been adopted, suggesting a framework capable of extracting numerical predictions and analytical equations, respectively, exclusively derived from data. The database of the reduced flow rates W used in the current ML framework has been obtained using kinetic modeling and it refers to nonlinear flows through circular tubes (tube length over radius $$l \in [0,5]$$ l ∈ [ 0 , 5 ] and downstream over upstream pressure $$p \in [0,0.9]$$ p ∈ [ 0 , 0.9 ] ) in a very wide range of the gas rarefaction parameter $$\delta \in [0,10^3]$$ δ ∈ [ 0 , 10 3 ] . The accuracy of both RFR and SR models is assessed using statistical metrics, as well as the relative error between the ML predictions and the kinetic database. The predictions obtained by RFR show very good fit on the simulation data, having a maximum absolute relative error of less than $$12.5\%$$ 12.5 % . Various expressions of the form of $$W=W(p,l,\delta )$$ W = W ( p , l , δ ) with different accuracy and complexity are acquired from SR. The proposed equation, valid in the whole range of the relevant parameters, exhibits a maximum absolute relative error less than $$17\%$$ 17 % . To further improve the accuracy, the dataset is divided into three subsets in terms of $$\delta$$ δ and one SR-based closed-form expression of each subset is proposed, achieving a maximum absolute relative error smaller than $$9\%$$ 9 % . Very good performance of all proposed equations is observed, as indicated by the obtained accuracy measures. Overall, the present ML-predicted data may be very useful in gaseous microfluidics and vacuum technology for engineering purposes.

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

Mohammad Nejad,

Peters,

Nedea

et al. 2024

Microfluid Nanofluid

View full text Add to dashboard Cite

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.

show abstract

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

Abstract: Modeling rarefied gas-solid surface interactions for Couette flow with different wall temperatures using an unsupervised machine learning technique. Physical Review E, 104(1-2), [015309].

Cited by 11 publications

References 42 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

Computation of flow rates in rarefied gas flow through circular tubes via machine learning techniques

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

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