Artificial Intelligence in Heat Transfer : Advances in Numerical Heat Transfer Volume VI

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Artificial Intelligence in Heat Transfer : Advances in Numerical Heat Transfer Volume VI

Abraham, J.P. (EDT)/ Gorman, J.M. (EDT)

ウェブストア価格 ¥34,241（本体¥31,129）
CRC Press（2025/05発売）
外貨定価 US$ 160.00
ポイント 311pt

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製本 Hardcover:ハードカバー版／ページ数 248 p.
言語 ENG
商品コード 9781032688107
DDC分類 621.4022

Full Description

Artificial Intelligence in Heat Transfer shows how artificial intelligence (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, nanofluids, thermal radiation, heat transfer through porous media (metal foam), and more.

Drawing on the collective expertise of leading researchers and experts in multiple fields, the book provides an in-depth understanding of the possibilities that emerge when these tools are applied to problems related to thermal sciences. AI is an ever-evolving discipline that has created new and groundbreaking opportunities to advance the mechanical engineering field, particularly in the area of numerical heat transfer. This volume, Advances in Numerical Heat Transfer, explores various ways AI is used in heat transfer to solve engineering problems.

This book will serve as an important resource for upper-level undergraduate students, researchers, engineers, and professionals, equipping them with the knowledge and inspiration to push the boundaries of the thermal sciences through AI-driven tools and techniques.

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.