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

IF 2.3 4区 工程技术 Q2 INSTRUMENTS & INSTRUMENTATION
Shahin Mohammad Nejad, Frank A. Peters, Silvia V. Nedea, Arjan J. H. Frijns, David M. J. Smeulders
{"title":"A hybrid Gaussian mixture/DSMC approach to study the Fourier thermal problem","authors":"Shahin Mohammad Nejad,&nbsp;Frank A. Peters,&nbsp;Silvia V. Nedea,&nbsp;Arjan J. H. Frijns,&nbsp;David M. J. Smeulders","doi":"10.1007/s10404-024-02719-x","DOIUrl":null,"url":null,"abstract":"<div><p>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 (<span>\\(\\hbox {H}_{2}\\)</span>) 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.</p></div>","PeriodicalId":706,"journal":{"name":"Microfluidics and Nanofluidics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10404-024-02719-x.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microfluidics and Nanofluidics","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10404-024-02719-x","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0

Abstract

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 (\(\hbox {H}_{2}\)) 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.

Abstract Image

研究傅立叶热问题的混合高斯混合物/DSMC 方法
在稀薄气体动力学中,散射核值得特别关注,因为它们包含了气固表面界面的物理和化学特性对气体散射过程影响的所有基本信息。然而,为了研究气固表面相互作用对流体流动大尺度行为的影响,这些散射核需要集成到更大尺度的模型中,如直接模拟蒙特卡罗(DSMC)。在这项工作中,利用高斯混合(GM)模型这种无监督机器学习方法,直接从分子动力学(MD)模拟数据中建立了单原子(Ar)和双原子(\(\hbox {H}_{2}\))气体的散射核。GM 散射核与纯 DSMC 求解器耦合,以研究具有两个平行壁的系统中的等温和非等温稀薄气体流。为了充分检验 GM 散射核与 DSMC 方法之间的耦合机制,本文考虑了 MD 粒子与 DSMC 粒子之间的一一对应关系。以 MD 结果为基准,对 GM-DSMC 的性能进行了评估,并将 Cercignani-Lampis-Lord(CLL)内核纳入 DSMC 仿真(CLL-DSMC)。对各种物理和随机参数的比较表明,GM-DSMC 方法的性能更好。特别是对于二原子系统,GM-DSMC 的性能优于 CLL-DSMC。GM-DSMC 方法的基本优越性证实了它作为一种多尺度模拟方法的潜力,可用于精确测量高度非平衡条件系统中的流场特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Microfluidics and Nanofluidics
Microfluidics and Nanofluidics 工程技术-纳米科技
CiteScore
4.80
自引率
3.60%
发文量
97
审稿时长
2 months
期刊介绍: Microfluidics and Nanofluidics is an international peer-reviewed journal that aims to publish papers in all aspects of microfluidics, nanofluidics and lab-on-a-chip science and technology. The objectives of the journal are to (1) provide an overview of the current state of the research and development in microfluidics, nanofluidics and lab-on-a-chip devices, (2) improve the fundamental understanding of microfluidic and nanofluidic phenomena, and (3) discuss applications of microfluidics, nanofluidics and lab-on-a-chip devices. Topics covered in this journal include: 1.000 Fundamental principles of micro- and nanoscale phenomena like, flow, mass transport and reactions 3.000 Theoretical models and numerical simulation with experimental and/or analytical proof 4.000 Novel measurement & characterization technologies 5.000 Devices (actuators and sensors) 6.000 New unit-operations for dedicated microfluidic platforms 7.000 Lab-on-a-Chip applications 8.000 Microfabrication technologies and materials Please note, Microfluidics and Nanofluidics does not publish manuscripts studying pure microscale heat transfer since there are many journals that cover this field of research (Journal of Heat Transfer, Journal of Heat and Mass Transfer, Journal of Heat and Fluid Flow, etc.).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信