应用机器学习算法预测 BN-金刚石/热油混合纳米流体的流变行为

IF 1.8 Q3 MECHANICS
Fluids Pub Date : 2024-01-09 DOI:10.3390/fluids9010020
Abulhassan Ali, Nawal Noshad, Abhishek Kumar, Suhaib Umer Ilyas, Patrick E. Phelan, M. Alsaady, R. Nasir, Yuying Yan
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引用次数: 0

摘要

由于纳米流体具有更强的热性能,近来其在传热应用中的使用大幅增加。因此,研究不同纳米悬浮液的流动行为以及流变性能对提高传热性能非常重要。本研究采用四种机器学习(ML)算法,即随机森林(RF)、梯度提升回归(GBR)、高斯回归(GR)和人工神经网络(ANN),预测了 BN-金刚石/导热油混合纳米流体的粘度与温度(25-65 °C)、颗粒浓度(0.2-0.6 wt.%)和剪切速率(1-2000 s-1)的函数关系。通过比较分析,采用了六种不同的误差矩阵来评估这些模型的性能。数据被随机分为训练数据和测试数据。为了更好地预测 700 个实验数据点,对算法进行了优化。虽然所有 ML 算法的 R2 值都大于 0.99,但 GBR 预测最准确,误差最小。这项研究表明,ML 算法对纳米流体的流变预测具有很高的准确性和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids
The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25–65 °C), particle concentration (0.2–0.6 wt.%), and shear rate (1–2000 s−1). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R2 values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids.
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来源期刊
Fluids
Fluids Engineering-Mechanical Engineering
CiteScore
3.40
自引率
10.50%
发文量
326
审稿时长
12 weeks
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