Igor Rychlik, Sayan Gupta, Georg Lindgren

eBook, ePUB

Fatigue Prediction for Random Loads (eBook, ePUB)

Using the Rainflow Method

Beschreibung

Fatigue Prediction for Random Loads serves as a comprehensive treatise for methods for fatigue estimation and fatigue life prediction in randomly excited structural systems using the rainflow cycle counting method.

Fatigue is an important mode of material degradation in structural components subjected to vibrations and a reliable estimation of their fatigue life span, and it is a key consideration in the design and development of such systems. This book uses advanced concepts of probability theory, random variables and random processes to develop spectral-based methods and formulae for predicting expected fatigue damage and expected fatigue life. The developments presented here bypass the need for a computationally expensive rainflow cycle counting that is usually adopted in time domain approaches.

This book is aimed towards researchers and industry practitioners working in the intersectional areas of mechanics and applied mathematics and is expected to be particularly useful for applications on problems in the fields of wind engineering, offshore engineering, ship research and routing, aerospace engineering, automotive engineering and machine dynamics.

Technische Details

• Geräte: eReader
• mit Kopierschutz
• Größe: 5.63MB

Barrierefreiheit

• Text-to-Speech

Autor

Igor Rychlik is Professor Emeritus at Chalmers University of Technology, Sweden. He earned a PhD in mathematical statistics at Lund University, Sweden, 1986. During 1988-1990 he was a post-doctoral student at the Department of Statistics, Colorado State University, and in 1995 he was a visiting professor at the University of Queensland, Brisbane, Australia. He became a Professor of Mathematical Statistics at Lund in 1999 and from 2007 at Chalmers University of Technology, Göteborg. There he also worked as a scientific advisor to the Fraunhofer-Chalmers Centre for industrial mathematics. Since 2017 he has been a Professor Emeritus. His main interest is in engineering applications of random processes and fields and in the modelling of complex environmental loads, wind, sea waves and vehicle operating conditions. He is a coauthor of Guide to Load Analysis for Durability in Vehicle Engineering and Probability and Risk Analysis: An Introduction for Engineers.

Sayan Gupta is a Professor at the Indian Institute of Technology Madras in the Department of Applied Mechanics. He graduated with a degree in civil engineering from Jadavpur University Kolkata in 1997, earned a PhD at the Indian Institute of Science Bangalore in 2005 and was a post-doctoral student at the Technical University of Delft from 2005 to 2007. He has held visiting positions at Waterloo, Lund, and Chalmers. Since 2007, he has been working at IIT Madras, where he leads the Uncertainty Lab. He is the principal investigator for the Center for Complex Systems and Dynamics, IIT Madras, and he is the coordinator for the degree program on Complex Systems and Dynamics. His primary research interests include non-linear dynamics and stochastic mechanics.

Georg Lindgren is Professor Emeritus in the Engineering Faculty at Lund University, Sweden. He earned a PhD in mathematical statistics at Lund in 1972 with a thesis inspired by an intriguing question regarding random load cycle ranges. After a period as an Associate Professor at Lund and Umeå, he became a Professor of Mathematical Statistics at Lund in 1986, and since 2006 he has been a Professor Emeritus. His main interest is random processes and their use in communication technology, marine and mechanical engineering and medicine. He is a coauthor of a 1983 research volume on the extremes and related properties of random sequences and processes and two research/advanced student texts on stationary stochastic processes. He is a member of the Royal Swedish Academy of Engineering Sciences (IVA).

Produktdetails

• Verlag: Taylor & Francis eBooks
• Erscheinungstermin: 22. Mai 2025
• Englisch
• ISBN-13: 9781040343463
• Artikelnr.: 73747493