Machine learning prediction of nanofluid thermal and flow characteristics - Doctor of Philosophy thesis submission

Aug 2, 2026

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Machine learning prediction of nanofluid thermal and flow characteristics is presented as a Doctor of Philosophy thesis submission at the University of Leeds. The work incorporates peer-reviewed article manuscripts and confirms original, theoretical, empirical, and modelling contributions completed during postgraduate research. The thesis includes published materials focused on predicting nanofluid heat transfer and properties such as heat transfer coefficients, thermal conductivity, viscosity, and modelling approaches. It also covers intellectual property acknowledgements and structured thesis sections across multiple chapters, with research support and supervision documented.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages186

Reading Time7 h 49 min

AccessFree to download

Intellectual property and publications

List of published work presented as thesis chapters

Predicting heat transfer coefficient accuracy using neural networks

Predicting thermal conductivity and viscosity for nanofluids using neural networks

List of other published work during the candidate's study

Conferences

THE AUTHOR

ACKNOWLEDGEMENTS

Machine learning prediction of nanofluid thermal and flow characteristics Ekene Jude Onyiriuka Submitted in accordance with the requirements for the degree of Doctor of Philosophy The University of Leeds School of Mechanical Engineering March 2024 Intellectual property and publications The thesis includes article manuscripts that have been written and peer-reviewed in academic journals, recognised to be relevant to the field of the candidate’s research and academically reputable. They have been presented as they appear in the published form and are self-contained. The papers present original work. The manuscripts included in the thesis report are findings based on original, theoretical, empirical work, and modelling that form a part of the research conducted during the candidate’s post-graduate research studies. The candidate confirms that the work submitted is his own. The candidates confirm that appropriate credit has been given where references have been made to the work of others. List of published work presented as thesis chapters Onyiriuka, Ekene Jude. "Predicting the accuracy of nanofluid heat transfer coefficient's computational fluid dynamics simulations using neural networks." Heat Transfer (2023) . [https://doi.org/10.1002/htj.22833](https://doi.org/10.1002/htj.22833) Onyiriuka, Ekene J. "Single phase nanofluid thermal conductivity and viscosity prediction using neural networks and its application in a heated pipe of a circular crosssection." Heat Transfer (2023) . [https://doi.org/10.1002/htj.22838](https://doi.org/10.1002/htj.22838) Onyiriuka, E. Predictive modelling of thermal conductivity in single-material nanofluids: a novel approach. Bull Natl Res Cent 47, 140 (2023) . [https://doi.org/10.1186/s42269-023-01115-9](https://doi.org/10.1186/s42269-023-01115-9) List of other published work during the candidate's study in University of Leeds Onyiriuka, E. Optimising Al2O3–water nanofluid. Bull Natl Res Cent 48, 2 (2024) . [https://doi.org/10.1186/s42269-023-01162-2](https://doi.org/10.1186/s42269-023-01162-2) Onyiriuka, E. Modeling nanofluid viscosity: comparing models and optimizing featureselection—a novel approach. Bull Natl Res Cent 47, 139 (2023) . [https://doi.org/10.1186/s42269-023-01114-w](https://doi.org/10.1186/s42269-023-01114-w) Onyiriuka, E. Modelling the thermal conductivity of nanofluids using a novel model of models approach. J Therm Anal Calorim (2023) . [https://doi.org/10.1007/s10973-023-](https://doi.org/10.1007/s10973-023-)[ ](https://doi.org/10.1007/s10973-023-)[12642-y](12642-y) Ewim, D. R. E. , Adelaja, A.OEwim, D. R. E. , Adelaja, A.O. , Onyiriuka, E.J. Modelling of heat transfer coefficients during condensation inside an enhanced inclined tube. JTherm Anal Calorim 146, 103–115 (2021) . [https://doi.org/10.1007/s10973-020-09930-](https://doi.org/10.1007/s10973-020-09930-)[ ](https://doi.org/10.1007/s10973-020-09930-)2 Ewim, D. R. , Shote, A. S. , Onyiriuka, E. J. , Adio, S. A. , & Kaood, A. Thermal performance of nano refrigerants: a short review. J Mech Eng Res Dev, 44, 89-115 (2021) . Ewim, D. R. E. , Okwu, M. O. , Onyiriuka, E. J. , Abiodun, A. S. , Abolarin, S. M. , & Kaood, A. A quick review of the applications of artificial neural networks (ANN) in the modelling of thermal systems (2021) . Conferences Ekene J Onyiriuka, Daniel R E Ewim, Sogo M Abolarin An optimization technique to identify simulation assumptions for various nanofluids using machine learning. Proceedings of the 17th International Heat Transfer Conference, IHTC-17, 14 – 18 August 2023 , Cape Town, South Africa This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. The right of Ekene Jude Onyiriuka to identify as Author of this work has been asserted by him in accordance with the copyright, Designs and Patents Act 1988. This format has been chosen since t

FAQs

What is the main research focus of the thesis?

The thesis focuses on machine learning prediction of nanofluid thermal and flow characteristics, including thermal and transport behavior relevant to heat transfer.

How is the thesis material structured?

The thesis is described as having three main sections: an introduction section (Chapter One to Three), a manuscript section (Chapter Four to Six), and a discussion section (Chapter Seven).

What types of work are included in the thesis?

The thesis includes article manuscripts based on original theoretical, empirical, and modelling work, along with published papers and conference contributions.

Aug 2, 2026

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