Machine learning-assisted analysis and prediction for the thermal effect of various working fluids in a vortex tube
Machine learning-assisted analysis and prediction for the thermal effect of various working fluids in a vortex tube
复制标题
DOI：
10.1002/apj.3005
复制
发表时间：
2023-11-13
影响因子：
1.8
通讯作者：
Chen，Guangming
中科院分区：
文献类型：
--
作者：
Wang，Zheng;Li，Nian;Chen，Guangming
随着涡流管在各个领域的广泛应用，定量探讨不同流体(天然流体、碳氢化合物等)的分离效果变得十分必要。在涡流管内，并促进其利用。本研究开发了一种新的方法来建立定量模型来预测不同流体在涡流管中的热效应。这些模型既基于工质的宏观性质，又基于分子尺度的微观分子描述符。数据集采用了11种碳氢化合物和氢氟烃制冷剂的数值模拟结果。通过随机森林特征分析，筛选和识别了3个操作条件、10个性质参数和115个分子描述符。利用人工神经网络(ANN)建模技术，建立了两类模型(微观模型和宏观模型)。结果确定了影响热效应的4个关键流体性质参数(比热比γ、热容与摩尔质量的乘积cp·M、运动粘度ν和导热系数λ)和9个分子描述符，并分别选择它们作为宏观和微观ANN模型建立的输入。这两种模型都显示出较高的相关系数(R>； .999)和相对较低的均方误差。当R600用于验证时，大部分相对误差小于10%，表明两种类型的模型都可以有效地预测其他流体的热效应。这些发现有助于更深入地了解涡流管的热力学效应，并为在各种应用中选择和优化工质提供了有价值的工具。
With the widespread application of vortex tube in various fields, it becomes essential to quantitatively explore the separation effect of different fluids (natural fluids, hydrocarbons, etc.) within the vortex tube and to promote its utilization. A new approach has been developed in this study to establish quantitative models for predicting the thermal effects of different fluids in a vortex tube. These models are based on both the macro properties of the working fluid and micro molecular descriptor through a molecular scale. A dataset of 11 numerical simulation results of hydrocarbons and hydrofluorocarbons refrigerants is employed. Three operating conditions, 10 property parameters, and 115 molecular descriptors are screened and identified using random forest feature analysis. Two types of models (micro and macro) have been developed by employing artificial neural network (ANN) modeling techniques. In the result, four key influencing fluid property parameters (the specific heat ratioγ, the multiplication of heat capacity and the molar weightcp·M, the kinematic viscosityν, and the thermal conductivityλ) and nine molecular descriptors in affecting the thermal effect are identified and respectively chosen as the input in the macro and the micro ANN model establishment. Both types of developed models show a high correlation coefficient (R> .999) and a comparatively low mean square error (MSE). When R600 is employed in the validation, most of the relative error is less than 10%, suggesting both types of models can work effectively in predicting the thermal effect for other fluid. The findings contribute to a deeper understanding of the thermodynamic effect of vortex tubes and provide a valuable tool for selecting and optimizing working fluids in various applications.