Abstract
Revolutionizing Heat Transfer: Nanofluids, Turbulators, and Machine Learning for Sustainable Energy Efficiency bridges the knowledge gap between traditional heat transfer enhancement techniques and innovative approaches employing nanofluids and turbulators. Users will find this to be an all-inclusive resource on the latest advancements in nanofluids, turbulators, and machine learning techniques for heat transfer enhancement that also includes detailed guidance on the synthesis, characterization, design, and optimization of these technologies. Using an interdisciplinary approach, this book serves as a valuable reference for researchers and practitioners working on heat transfer in energy applications and students studying related areas. There is a growing need for this resource as it addresses both the limitations of current heat transfer techniques while also providing sustainable solutions for a wide range of engineering applications.
| Original language | English |
|---|---|
| Title of host publication | Revolutionizing Heat Transfer |
| Subtitle of host publication | Nanofluids, Turbulators, and Machine Learning for Sustainable Energy Efficiency |
| Publisher | Elsevier |
| Pages | 1-301 |
| Number of pages | 301 |
| ISBN (Electronic) | 9780443315305 |
| ISBN (Print) | 9780443315312 |
| DOIs | |
| Publication status | Published - Jan 1 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
ASJC Scopus subject areas
- General Engineering
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