Application of Machine Learning Algorithms to Predict the Performance of Nanofluids in Heat Transfer Devices
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Abstract
Several machine learning algorithms are employed to predict the thermal conductivity of Al2O3/EG_Water nanofluids. Algorithms such as Support Vector Regression, Artificial Neural Network, and XGBoost are compared based on their ability to predict thermal conductivity. According to the findings, the XGBoost algorithm yields the most accurate predictions for the thermal conductivity of nanofluids at various temperatures, volume fractions, and EG-water mixing ratios. The accuracy of each model was assessed using its mean absolute error and R-squared (R2) values. The XGBoost algorithms achieved an R2 value of 0.99, indicating excellent performance. Therefore, this study concludes that the XGBoost algorithm can be effectively used for future predictions by significantly reducing experimental time and cost. Moreover, the algorithm's predictions demonstrated excellent accuracy and closely aligned with experimental data reported in the literature.
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