Introduction to Pipe Stress Analysis
S. Kannappan
John Wiley and Sons Inc | ISBN: 0471815896 | 1986-06-11 | PDF (OCR) | 246 pages | 27.9 Mb
Pipe stress analysis provides the necessary techniques for engineers to design piping systems without overstressing and overlaoding the piping components and connected equipment. The following terms from applied mechanics are briefly discussed (not defined) here to familirize the engineer with them...
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Failure Rate Modelling for Reliability and Risk focuses on reliability theory and, specifically, on the failure rate (the hazard rate, the force of mortality) modelling and its generalizations, on systems operating in a random environment and on repairable systems. The failure rate is one of the crucial probabilistic characteristics for a number of disciplines; including reliability, survival analysis, risk analysis and demography.
Failure Rate Modelling for Reliability and Risk presents a systematic study of the failure rate and related indices, and covers a number of important applications where the failure rate plays the major role. Applications in engineering systems are studied, together with some actuarial, biological and demographic examples.
Covering material previously available only in the journal literature, Failure Rate Modelling for Reliability and Risk provides a survey of this broad and interdisciplinary subject which will be invaluable to researchers and advanced students in reliability engineering and applied statistics, as well as to demographers, econometricians, actuaries and many other mathematically oriented researchers.
Written for:
Advanced undergraduate and postgraduate students in reliability engineering; researchers in reliability engineering; reliability engineers; system designers
Keywords:
Failure Rate
Intensity Processes
Reliability
Repairable Systems
Stochastic Processes
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As the Lead Reliability Engineer for Ford Motor Company, Guangbin Yang is involved with all aspects of the design and production of complex automotive systems. Focusing on real-world problems and solutions, Life Cycle Reliability Engineering covers the gamut of the techniques used for reliability assurance throughout a product's life cycle. Yang pulls real-world examples from his work and other industries to explain the methods of robust design (designing reliability into a product or system ahead of time), statistical and real product testing, software testing, and ultimately verification and warranting of the final product's reliability
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Expository accessible book, internationally known authors
A combination of the concepts subjective – or Bayesian – statistics and scientific computing, the book provides an integrated view across numerical linear algebra and computational statistics. Inverse problems act as the bridge between these two fields where the goal is to estimate an unknown parameter that is not directly observable by using measured data and a mathematical model linking the observed and the unknown.
Inverse problems are closely related to statistical inference problems, where the observations are used to infer on an underlying probability distribution. This connection between statistical inference and inverse problems is a central topic of the book. Inverse problems are typically ill-posed: small uncertainties in data may propagate in huge uncertainties in the estimates of the unknowns. To cope with such problems, efficient regularization techniques are developed in the framework of numerical analysis. The counterpart of regularization in the framework of statistical inference is the use prior information. This observation opens the door to a fruitful interplay between statistics and numerical analysis: the statistical framework provides a rich source of methods that can be used to improve the quality of solutions in numerical analysis, and vice versa, the efficient numerical methods bring computational efficiency to the statistical inference problems.
This book is intended as an easily accessible reader for those who need numerical and statistical methods in applied sciences.
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This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extend the standard models to specialized data analysis situations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation. Numerous examples from the social, biological and physical sciences show how to implement these methodologies in practice.
Monte Carlo summaries of posterior distributions play an important role in Bayesian data analysis. The open-source R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example R-code is provided throughout the text. Much of the example code can be run “as is”' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book.
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Greek code for Seismic Resistant Structures - EAK2000 (English Language)
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Computer Methods in Applied Mechanics and Engineering (November 2009)
Volume 198, Issues 49-52, (1 November 2009) PDF, 12 MB
The development of computer methods for the solution of scientific and engineering problems governed by the laws of mechanics was one of the great scientific and engineering achievements of the second half of the 20th century, with a profound impact on science and technology.
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The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving
Editors
Dr. R.A. Adey
Professor A.K. Noor
Professor B.H.V Topping
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1. File can be downloaded more than 10 times, "unlimited" and it only gets deleted if nobody has downloaded the file in 90 days or the file got reported.
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Stochastics: Introduction to Probability and Statistics
This book is a translation of the third edition of the well accepted German textbook 'Stochastik', which presents the fundamental ideas and results of both probability theory and statistics, and comprises the material of a one-year course. The stochastic concepts, models and methods are motivated by examples and problems and then developed and analysed systematically.
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