https://doi.org/10.1140/epjp/s13360-025-07139-6
Regular Article
Modeling and optimizing the Fe3O4/water nanofluid pool boiling process on the copper block's heating surface using the artificial neural network and the multi-objective genetic algorithm
1
Department of Mechanical Engineering, Na.C., Islamic Azad University, Najafabad, Iran
2
Power/Mechanical Engineering Department, Engineering Technical College of Al-Najaf, Al-Furat Al-Awast Technical University (ATU), 54001, Al-Najaf, Iraq
3
Department of Mechanical Engineering, SR.C., Islamic Azad University, Tehran, Iran
a
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Received:
1
October
2025
Accepted:
29
November
2025
Published online:
16
December
2025
Abstract
This study presents an integrated framework combining an artificial neural network (ANN) and a multi-objective genetic algorithm (MOGA) to model, interpret, and optimize the pool boiling of Fe3O4/water nanofluid. Using a validated experimental dataset (Abdollahi et al. in Appl Therm Eng 111:1101–1110, 2017), an ANN model was developed to predict the boiling heat transfer coefficient (BHTC) and wall superheat based on heat flux and nanoparticle concentration. The optimization aimed to simultaneously maximize the BHTC while minimizing wall superheat. Methodological rigor was ensured through tenfold cross-validation, robustness analysis, and benchmarking, confirming the model's high accuracy (R2 > 0.99). Moving beyond a black-box approach, a feature sensitivity analysis revealed concentration as the dominant factor for BHTC. The model's predictions were validated against the Rohsenow correlation and used to physically interpret the process, capturing the competing mechanisms of nucleation site enhancement and thermal resistance. The MOGA identified a Pareto-optimal set of operating conditions, with an optimal concentration of 0.09–0.1 vol% that was experimentally validated. This work demonstrates a powerful methodology for translating complex experimental data into physical insights and actionable design parameters.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
