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

IF 2.3 4区 工程技术 Q2 INSTRUMENTS & INSTRUMENTATION
F. Sofos, C. Dritselis, S. Misdanitis, T. Karakasidis, D. Valougeorgis
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引用次数: 0

Abstract

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 \(l \in [0,5]\) and downstream over upstream pressure \(p \in [0,0.9]\)) in a very wide range of the gas rarefaction parameter \(\delta \in [0,10^3]\). 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 \(12.5\%\). Various expressions of the form of \(W=W(p,l,\delta )\) 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 \(17\%\). To further improve the accuracy, the dataset is divided into three subsets in terms of \(\delta\) and one SR-based closed-form expression of each subset is proposed, achieving a maximum absolute relative error smaller than \(9\%\). 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.

Abstract Image

利用机器学习技术计算稀薄气体流经圆管的流速
动力学理论和模型已被证明非常适用于计算稀薄气体管道流动的流量,但它们的计算成本很高,更重要的是在微系统和真空系统的设计和优化中不实用。在处理此类系统时,为了降低计算成本并提高可访问性,利用机器学习(ML)采用了两种有效的方法。更具体地说,采用了随机森林回归(RFR)和符号回归(SR),提出了一个能够分别从数据中提取数值预测和分析方程的框架。目前ML框架中使用的降低流量W数据库是通过动力学建模获得的,它指的是在很宽的气体稀薄参数\(\delta \in [0,10^3]\)范围内通过圆管(管长除以半径\(l \in [0,5]\)和下游除以上游压力\(p \in [0,0.9]\))的非线性流动。使用统计指标评估RFR和SR模型的准确性,以及ML预测与动力学数据库之间的相对误差。RFR预测结果与模拟数据拟合良好,最大绝对相对误差小于\(12.5\%\)。由sr得到了不同精度和复杂度的\(W=W(p,l,\delta )\)形式的表达式。所提出的方程在所有相关参数范围内都有效,其最大绝对相对误差小于\(17\%\)。为了进一步提高准确率,将数据集按\(\delta\)划分为三个子集,并对每个子集提出一个基于sr的封闭形式表达式,最大绝对相对误差小于\(9\%\)。所有提出的方程都有很好的性能,正如所获得的精度测量所表明的那样。总的来说,目前的机器学习预测数据可能对气体微流体和真空技术的工程用途非常有用。
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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.).
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