Multifidelity Modeling in Vibration Analysis teaches users how to make predictions about physical systems in a computationally inexpensive manner.
Multifidelity Modeling in Vibration Analysis teaches users how to make predictions about physical systems in a computationally inexpensive manner.
The aim of this book is to introduce the concept of multifidelity modeling through structural dynamics case studies. The book focuses on vibration analysis problems to illustrate how multifidelity methods work. Two key methods — the response surface methods and the co-kriging method — are discussed to present the reader with state of the art practices that are easy to implement. Also, two different physics-based mathematical models of a system, the Euler-Bernoulli beam model and the Timoshenko beam model, are used at two disparate levels of discretization.
This book will help graduate students, researchers, and scientists who are interested in applying multifidelity models to uncertainty quantification, optimization, and robust and reliability-based design problems of vibration of engineering systems.
Chapter
1. Multifidelity Models
Chapter
2. Vibration Analysis
Chapter
3. Multifidelity Physics with Response Surfaces
Chapter
4. Multifidelity Numerics with Response Surfaces
Chapter
5. Multifidelity Physics with Co-Kriging
Chapter
6. Multifidelity Numerics with Co-Kriging
Dr. Ranjan Ganguli is the founder of Digital Predilection LLC, a digital higher education company. He received his MS and PhD degrees from the Department of Aerospace Engineering at the University of Maryland, College Park, USA in 1991 and 1994, respectively, and his B.Tech degree in Aerospace Engineering from the Indian Institute of Technology, Kharagpur, in 1989. He was a Professor in the Aerospace Engineering Department of the Indian Institute of Science, Bangalore from 2000-2021. He worked at Viasat, San Diego, on satellite internet load balancing using Shannons entropy optimization during 2022-2023 and at Pratt and Whitney, East Hartford, on engine diagnostics using machine learning during 1998-2000. He has completed sponsored research projects for companies such as Boeing, Pratt and Whitney, Honeywell, HAL and others. His research is published in refereed journals and conferences. He has authored books titled Digital Twin, Isospectral Vibrating Systems, Gas Turbine Diagnostics, and Engineering Optimization, among others. He is a Fellow of the American Society of Mechanical Engineers, an Associate Fellow of the American Institute of Aeronautics and Astronautics, a Senior Member of the IEEE, a Fellow of the Indian National Academy of Engineering and the National Academy of Sciences. He also received the Alexander von Humboldt Fellowship and the Fulbright Fellowship in 2007 and 2011, respectively. He has held visiting scientist positions in Germany, France and South Korea. He received the Royal Academy of Engineering Distinguished Visiting Fellowship in 2021.
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