Amir Musa Abazari, As'ad Alizadeh, Mostafa Barzegar Gerdroodbary, Sajad Salavatidezfouli

eBook, ePUB

Kinetic Energy Harvesters (eBook, ePUB)

Principles, Technologies, and Applications

Beschreibung

Kinetic Energy Harvesters: Principles, Technologies, and Applications presents a comprehensive analysis of the five types of kinetic energy harvesters, offering readers a single resource to learn about the principles, technologies, and applications.The opening chapters of the book provide a concise review of free and forced vibration analysis, as well as Multi Degree of Freedom systems. The subsequent chapters systematically examine the five types of energy harvesters, piezoelectric, electromagnetic, magnetostrictive, electrostatic, and triboelectric. Within the chapters, each ambient vibration phenomenon is described in detail, followed by an explanation of the relevant principles. Analytical analyses of kinetic energy and its conversion to electrical energy are then presented, alongside the governing equations, and a discussion of the technologies applications. Finally, MATLAB code is provided for programming calculations.A comprehensive resource on kinetic energy harvesting, Kinetic Energy Harvesters: Principles, Technologies, and Applications is an invaluable resource for anyone working on energy harvesting technologies, energy conversion, or the diverse range of applications for these technologies. - Includes all five mechanisms for harvesting kinetic energy, including piezoelectric, electromagnetic, magnetostrictive, electrostatic, and triboelectric - Explains the fundamental principles and rules of all kinetic energy harvesting technologies - Provides the governing equations of energy harvesting technologies acquired by Frequency and Time-dependent Analyses as well as investigations of how harvested voltage, current, and power are varied by parameter changes - Systematically reviews the applications of the different types of energy harvesting systems - Contains MATLAB Programming and Simulink Examples

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Autor

A.M. Abazari received BS degree from Tabriz University in 2008 and M.Sc. degree from Urmia University in 2010 in mechanical engineering. He went to Isfahan University of Technology (IUT) in 2011 and received his Ph.D. in 2016; He has also spent a research period at EPFL, Switzerland. He currently serves as an associate professor in the mechanical engineering department at Urmia University. His current research interests are in the fields of new technologies for energy harvesting, sensing and biomechanics: design, fabrication, and characterization.Dr. As'ad Alizadeh is a academic staff in the college of engineering, Cihan University-Erbil, Kurdistan Region, Iraq. He has a Ph.D. degree in Mechanical Engineering (Energy Conversion). His research interest includes Heat Transfer , Fluid Mechanics , Renewable Energy and Machine Learning . Dr. As'ad Alizadeh has published many articles in these fields in scientific journals. Dr. As'ad Alizadeh has been listed among the top 2% of scientists worldwide according to the Stanford ranking in 2024.Dr. Mostafa Barzegar Gerdroodbary is currently engaged as a Visiting Researcher at the Universidade da Beira Interior in Covilhã, Portugal. With over 10 years of experience in the mechanical and aerospace Engineering field, he has established expertise in computational fluid dynamics (CFD) modeling for various engineering challenges. His current research interests encompass a broad spectrum, including biomechanics, machine learning, renewable energy, and heat transfer. Recognized for his significant contributions to the scientific community, Dr. Barzegar Gerdroodbary has been listed among the top 2% of scientists worldwide according to the Stanford ranking since 2019. His scholarly output includes over 100 journal articles and three authored books, all published through prestigious journals and publishers.Dr. Salavatidezfouli is a researcher at the International School for Advanced Studies (SISSA) in Trieste, Italy. His research interests include applied mathematics, model order reduction, and scientific machine learning. Dr. Salavatidezfouli has contributed to numerous publications and conferences in these fields.

Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 387
• Erscheinungstermin: 7. Februar 2025
• Englisch
• ISBN-13: 9780443247170
• Artikelnr.: 73284430