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首页> 外文期刊>Heat transfer >Numerical and machine learning analyses of entropy generation in an unsteady squeezing flow of copper/aluminum oxide/water hybrid nanofluid
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Numerical and machine learning analyses of entropy generation in an unsteady squeezing flow of copper/aluminum oxide/water hybrid nanofluid

机译：铜/铝氧化铝/水杂交纳米流体不稳定挤压流动中熵生成的数值和机器学习分析

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摘要

Several studies have been carried out on the squeezing flow of mono-nanofluids. However, this study investigates numerical and machine learning analyses of entropy generated in an unsteady squeezing flow of a copper-aluminum oxide/water hybrid nanofluid. Numerical analysis and the flow model simulation are done using the hybridization of Chebyshev pseudos-pectral and quasilinearization methods. The results show that the magnetic force is the most significant in the entropy generation number. A support vector machine learning model is introduced to calculate the average entropy generation number. The machine learning model's accuracy is measured using known performance metrics of regression models. The root mean square error is obtained as 4.1184, the mean absolute error as 1.8776, and the coefficient of determination (R2) as 0.995. Furthermore, the Hartman and Eckert numbers are identified to be highly positively correlated to the entropy generation number. However, for increasing values of the Hartman number, the temperature distribution between the two parallel plates decreases. We found that the temperature of copper/aluminum oxide/water nanofluid is greater than that of copper/water nanofluid, and the percentage difference between the temperature at the lower plate is estimated to be between 6% and 9% for Ha G [0, 25].
机译：已经在单纳米流体的挤压流动上进行了几项研究。然而，本研究调查了在铜 - 铝氧化铝/水杂交纳米流体的不稳定挤压流中产生的熵的数值和机器学习分析。使用Chebyshev Pseudos-Pectral和Quasilizization方法的杂交来完成数值分析和流动模型模拟。结果表明，磁力是熵生成数中最重要的。引入支持向量机学习模型来计算平均熵生成数。使用回归模型的已知性能度量来测量机器学习模型的准确性。作为4.1184，平均绝对误差为1.8776的根均方误差，以及测定系数（R2）为0.995。此外，识别Hartman和Eckert编号与熵生成数高度呈正相关。然而，对于哈特曼数的增加，两个平行板之间的温度分布降低。我们发现，铜/氧化铝/水纳米流体的温度大于铜/水纳米流体的温度，估计下板的温度与HA G的温度之间的百分比差[0， 25]。

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