Artificial Intelligence in Heat Transfer

Advances in Numerical Heat Transfer Volume VI
Herausgeber: Gorman, J. M.; Abraham, J. P.

• Gebundenes Buch

Produktbeschreibung

This book shows how AI tools and techniques, such as artificial neural networks, machine learning algorithms, genetic algorithms, etc., provide practical benefits specific to thermal sciences. It presents case studies involving heat and mass transfer, multi-objective optimization, conjugate heat transfer, and more.

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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 344
• Erscheinungstermin: 8. Mai 2025
• Englisch
• Abmessung: 234mm x 156mm
• ISBN-13: 9781032688107
• ISBN-10: 1032688106
• Artikelnr.: 72212058

Autorenporträt

J.P. Abraham is a professor of thermal sciences at the University of St. Thomas School of Engineering, Minnesota, and the current editor-in-chief of Numerical Heat Transfer. His area of research includes thermodynamics, heat transfer, fluid flow, numerical simulation, and energy. After gaining his doctorate at the University of Minnesota in 2002, he joined St. Thomas as an adjunct instructor, later becoming a full-time faculty member. He has produced over 400 publications, books, book chapters, conference presentations, and patents in areas including biological heat transfer and fluid flow, biomedical device design, energy, burn injuries, climate change, fundamental heat transfer and fluid mechanics, and manufacturing processes. J.M. Gorman is currently an independent consultant in the mechanical engineering field. In addition to teaching as an adjunct professor, he is also a part of several startup companies ranging from biomedical, automotive, and novel heat recovery systems. After receiving his doctorate at the University of Minnesota in 2014, he was a research associate at the University of Minnesota until 2020. His research encompasses all facets of mechanical engineering, and his teaching is focused on modeling and numerical simulation in the thermal sciences. He has published over 60 papers in archival journals, along with five book chapters. He has also been a serial editor of Advances in Heat Transfer and the Advances in Numerical Heat Transfer book series.

Inhaltsangabe

1. Physics-informed neural networks for solving partial differential
equations. 2. Multi-Objective Optimization of Heat Transfer Problems. 3.
CFD/HT Simulations and DNN Modelling of Conjugate Heat Transfer in Metal
Foams. 4. Integrating Artificial Intelligence in Nanofluid Heat Transfer: A
Deep Dive into Artificial Intelligence Applications. 5. Developing an
Artificial Neural Network Algorithm for Heat and Mass Transfer Assessment
in Ternary Hybrid Nanofluid Flow. 6. Physics Informed Deep Learning
Approaches for Industrial Heat Exchangers. 7. AI based Analysis for
Optimizing Radiative Jeffery-Hamel Flow for Cross-Diffusion Effects: A
Physics Informed Machine Learning. 8. Machine Learning Process on Double
Diffusive Convection in a Parallelogram Shaped Cavity.