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