Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy

Abstract The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hy...

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出版年:	Scientific Reports
主要な著者:	Tao Hai, Ali Basem, As’ad Alizadeh, Pradeep Kumar Singh, Husam Rajab, Chemseddine Maatki, Nidhal Becheikh, Lioua Kolsi, Narinderjit Singh Sawaran Singh, H. Maleki
フォーマット:	論文
言語:	英語
出版事項:	Nature Portfolio 2025-01-01
主題:	Ternary hybrid nanofluid Machine learning Multi-objective optimization Multi-criteria decision-making Computational intelligence Thermophysical properties
オンライン･アクセス:	https://doi.org/10.1038/s41598-025-85236-3