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

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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Bibliographic Details
Main Authors: Tao Hai, Ali Basem, As’ad Alizadeh, Pradeep Kumar Singh, Husam Rajab, Chemseddine Maatki, Nidhal Becheikh, Lioua Kolsi, Narinderjit Singh Sawaran Singh, H. Maleki
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Ternary hybrid nanofluid
Machine learning
Multi-objective optimization
Multi-criteria decision-making
Computational intelligence
Thermophysical properties
Online Access:https://doi.org/10.1038/s41598-025-85236-3
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https://doi.org/10.1038/s41598-025-85236-3

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