Balaji Srinivasan – författare
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4 produkter
4 produkter
Häftad, Engelska, 2020
1 378 kr
Skickas inom 10-15 vardagar
Heat Transfer Engineering: Fundamentals and Techniques reviews the core mechanisms of heat transfer and provides modern methods to solve practical problems encountered by working practitioners, with a particular focus on developing engagement and motivation. The book reviews fundamental concepts in conduction, forced convection, free convection, boiling, condensation, heat exchangers and mass transfer succinctly and without unnecessary exposition. Throughout, copious examples drawn from current industrial practice are examined with an emphasis on problem-solving for interest and insight rather than the procedural approaches often adopted in courses.
The book contains numerous important solved and unsolved problems, utilizing modern tools and computational sources wherever relevant. A subsection on common issues and recent advances is presented in each chapter, encouraging the reader to explore a greater diversity of problems.
Reveals physical solutions alongside their application in practical problems, with an aim of generating interest from reality rather than dry exposition Reviews pertinent, contemporary computational tools, including emerging topics such as machine learning Describes the complexity of modern heat transfer in an engaging and conversational style, greatly adding to the uniqueness and accessibility of the book
E-bok
Engelska, 20201 923 kr
Läs direkt efter köp
Heat Transfer Engineering: Fundamentals and Techniques reviews the core mechanisms of heat transfer and provides modern methods to solve practical problems encountered by working practitioners, with a particular focus on developing engagement and motivation. The book reviews fundamental concepts in conduction, forced convection, free convection, boiling, condensation, heat exchangers and mass transfer succinctly and without unnecessary exposition. Throughout, copious examples drawn from current industrial practice are examined with an emphasis on problem-solving for interest and insight rather than the procedural approaches often adopted in courses.The book contains numerous important solved and unsolved problems, utilizing modern tools and computational sources wherever relevant. A subsection on common issues and recent advances is presented in each chapter, encouraging the reader to explore a greater diversity of problems.- Reveals physical solutions alongside their application in practical problems, with an aim of generating interest from reality rather than dry exposition- Reviews pertinent, contemporary computational tools, including emerging topics such as machine learning- Describes the complexity of modern heat transfer in an engaging and conversational style, greatly adding to the uniqueness and accessibility of the book
Häftad, Engelska, 2026
1 976 kr
Skickas inom 10-15 vardagar
Machine Learning and Bayesian Methods in Inverse Heat Transfer offers a comprehensive exploration of inverse problems in heat transfer, blending classical techniques with modern advancements in machine learning and Bayesian methods. This essential guide provides a hands-on approach with practical examples, making complex concepts accessible to readers seeking to deepen their understanding of this critical field. The text covers essential topics including Introduction to Inverse Problems, Statistical Description of Errors and General Approach, Classical Techniques, Bayesian Methods, and a Machine Learning Approach to Inverse Problems. Readers will explore key concepts such as Gaussian distribution, linear and non-linear regression, Gauss-Newton algorithm, Tikhonov regularization, and more, gaining a solid foundation in applying these methods to real-world heat transfer scenarios. For engineers, scientists, senior undergraduates, graduates, and researchers in heat transfer and related fields, this book serves as a vital resource. By offering clear explanations, practical examples, and MATLAB codes, it empowers readers to tackle inverse problems with confidence. Whether readers are practicing engineers or graduate students specializing in heat and mass transfer, this book equips them with the tools and knowledge to excel and further advances in their field.Emphasizes a machine learning approach to solving inverse heat transfer problemsProvides detailed explanations of fundamental scientific concepts in a clear, precise mannerIntegrates modern techniques with traditional methods to provide comprehensive understandingOffers practical examples throughout, allowing readers to apply theoretical knowledge to real-world scenarios, enhancing learning and advancing interdisciplinary applicationsSupports sustainability and responsible energy consumption -- especially UN SDGs 4, 7, 11, 12, 13, and 15 -- inverse heat transfer problems are important for researchers advancing efficient energy utilization
E-bok
Engelska, 20262 500 kr
Läs direkt efter köp
Machine Learning and Bayesian Methods in Inverse Heat Transfer offers a comprehensive exploration of inverse problems in heat transfer, blending classical techniques with modern advancements in machine learning and Bayesian methods. This essential guide provides a hands-on approach with practical examples, making complex concepts accessible to readers seeking to deepen their understanding of this critical field. The text covers essential topics including Introduction to Inverse Problems, Statistical Description of Errors and General Approach, Classical Techniques, Bayesian Methods, and a Machine Learning Approach to Inverse Problems. Readers will explore key concepts such as Gaussian distribution, linear and non-linear regression, Gauss-Newton algorithm, Tikhonov regularization, and more, gaining a solid foundation in applying these methods to real-world heat transfer scenarios. For engineers, scientists, senior undergraduates, graduates, and researchers in heat transfer and related fields, this book serves as a vital resource. By offering clear explanations, practical examples, and MATLAB codes, it empowers readers to tackle inverse problems with confidence. Whether readers are practicing engineers or graduate students specializing in heat and mass transfer, this book equips them with the tools and knowledge to excel and further advances in their field. - Emphasizes a machine learning approach to solving inverse heat transfer problems- Provides detailed explanations of fundamental scientific concepts in a clear, precise manner- Integrates modern techniques with traditional methods to provide comprehensive understanding- Offers practical examples throughout, allowing readers to apply theoretical knowledge to real-world scenarios, enhancing learning and advancing interdisciplinary applications- Supports sustainability and responsible energy consumption -- especially UN SDGs 4, 7, 11, 12, 13, and 15 -- inverse heat transfer problems are important for researchers advancing efficient energy utilization