Statistical Analysis and Accurate Prediction of Thermophysical Properties of ZnO-MWCNT/EG-Water Hybrid Nanofluid Using Several Artificial Intelligence Methods

IF 2.9 4区 综合性期刊 Q1 Multidisciplinary
Mohammad Shoaib Zamany, Amir Taghavi Khalil Abad
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引用次数: 0

Abstract

This paper presents a statistical analysis and modeling of the thermophysical properties of ZnO-MWCNT/EG-water hybrid nanofluid using three artificial intelligence models, including multilayer perceptron neural network, radial basis function neural networks, and least square support vector machine (LSSVM). The thermal conductivity of the nanofluid was modeled using experimental data, and statistical parameters such as R-squared (R2), average absolute relative deviation (AARD %), root mean squared error, and standard deviation were employed to investigate the accuracy of the proposed models. The R2 values of 0.9926, 0.9951, and 0.9866 and AARD% values of 0.4996%, 0.3532%, and 0.6013% show the accuracy of the models for respective MLP, RBF, and LSSVM models. Among these models, the RBF model shows the best accuracy. The study demonstrates the potential of artificial intelligence methods in predicting the thermophysical properties of nanofluids, which can help minimize experimental time and cost for future work.

Abstract Image

利用多种人工智能方法对 ZnO-MWCNT/EG-Water 混合纳米流体的热物理性质进行统计分析和精确预测
本文利用多层感知器神经网络、径向基函数神经网络和最小平方支持向量机(LSSVM)等三种人工智能模型对 ZnO-MWCNT/EG 水混合纳米流体的热物理性质进行了统计分析和建模。利用实验数据对纳米流体的导热性能进行建模,并采用 R 平方(R2)、平均绝对相对偏差(AARD %)、均方根误差和标准偏差等统计参数来考察所提模型的准确性。R2 值分别为 0.9926、0.9951 和 0.9866,AARD% 值分别为 0.4996%、0.3532% 和 0.6013%,这表明了 MLP、RBF 和 LSSVM 模型的准确性。在这些模型中,RBF 模型的准确率最高。这项研究证明了人工智能方法在预测纳米流体热物理性质方面的潜力,有助于最大限度地减少未来工作的实验时间和成本。
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来源期刊
Arabian Journal for Science and Engineering
Arabian Journal for Science and Engineering 综合性期刊-综合性期刊
CiteScore
5.20
自引率
3.40%
发文量
0
审稿时长
4.3 months
期刊介绍: King Fahd University of Petroleum & Minerals (KFUPM) partnered with Springer to publish the Arabian Journal for Science and Engineering (AJSE). AJSE, which has been published by KFUPM since 1975, is a recognized national, regional and international journal that provides a great opportunity for the dissemination of research advances from the Kingdom of Saudi Arabia, MENA and the world.
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