Probability Models in Engineering and Science
1st Edition
Published on 1. June 2005
Book
Hardback
760 pages
978-0-8247-2315-6 (ISBN)
Description
Certainty exists only in idealized models. Viewed as the quantification of uncertainties, probabilitry and random processes play a significant role in modern engineering, particularly in areas such as structural dynamics. Unlike this book, however, few texts develop applied probability in the practical manner appropriate for engineers.
Probability Models in Engineering and Science provides a comprehensive, self-contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and while doing so, develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations and an engaging, human connection to the subject, achieved through short biographies of some of the key people in the field. End-of-chapter problems help solidify understanding and footnotes to the literature expand the discussions and introduce relevant journals and texts.
This book builds the background today's engineers need to deal explicitly with the scatter observed in experimental data and with intricate dynamic behavior. Designed for undergraduate and graduate coursework as well as self-study, the text's coverage of theory, approximation methods, and numerical methods make it equally valuable to practitioners.
Probability Models in Engineering and Science provides a comprehensive, self-contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and while doing so, develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations and an engaging, human connection to the subject, achieved through short biographies of some of the key people in the field. End-of-chapter problems help solidify understanding and footnotes to the literature expand the discussions and introduce relevant journals and texts.
This book builds the background today's engineers need to deal explicitly with the scatter observed in experimental data and with intricate dynamic behavior. Designed for undergraduate and graduate coursework as well as self-study, the text's coverage of theory, approximation methods, and numerical methods make it equally valuable to practitioners.
More details
Language
English
Place of publication
New York
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Undergraduate and graduate students, practicing engineers, and scientists in a wide variety of disciplines, particularly mechanical engineering, mechanics, systems and controls, and vibration
Illustrations
27 s/w Tabellen, 247 s/w Abbildungen
27 Tables, black and white; 247 Illustrations, black and white
Dimensions
Height: 229 mm
Width: 152 mm
Weight
1134 gr
ISBN-13
978-0-8247-2315-6 (9780824723156)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Haym Benaroya | Seon Mi Han | Mark Nagurka
Probabilistic Models for Dynamical Systems
Book
05/2013
2nd Edition
CRC Press
€154.75
Article is exhausted, reprint undefined
Persons
Content
INTRODUCTION
Applications
Units
Organization of the Text
Problems
EVENTS AND PROBABILITY
Sets
Probability
Concluding Summary
Problems
RANDOM VARIABLE MODELS
Probability Distribution Function
Probability Density Function
Mathematical Expectation
Variance
USEFUL PROBABILITY DENSITIES
Two Random Variables
Concluding Summary
Problems
FUNCTIONS OF RANDOM VARIABLES
Exact Functions of One Variable
Functions of Two or More RVs
General Case
Approximate Analysis
Monte Carlo Method
Concluding Summary
Problems
The Standard Normal Table
RANDOM PROCESSES
Basic Random Process Descriptors
Ensemble Averaging
Stationarity
Derivatives of Stationary Processes
Fourier Series and Fourier Transforms
Harmonic Processes
Power Spectra
Fourier Representation of a Random Process
Borgman's Method of Frequency Discretization
Concluding Summary
Problems
SINGLE DEGREE OF FREEDOM DYNAMICS
Motivating Examples
Deterministic SDoF Vibration
SDoF: The Response to Random Loads
Response to Two Random Loads
Concluding Summary
Problems
MULTI DEGREE OF FREEDOM VIBRATION
Deterministic Vibration
Response to Random Loads
Periodic Structures
Inverse Vibration
Random Eigenvalues
Concluding Summary
Problems
CONTINUOUS SYSTEM VIBRATION
Deterministic Continuous Systems
Sturm-Liouville Eigenvalue Problem
Deterministic Vibration
Random Vibration of Continuous System
Beams with Complex Loading
Concluding Summary
Problems
RELIABILITY
Introduction
First Excursion Failure
Fatigue Life Prediction
Concluding Summary
Problems
NONLINEAR DYNAMIC MODELS
Examples of Nonlinear Vibration
Fundamental Nonlinear Equations
Statistical Equivalent Linearization
Perturbation Methods
The van der Pol Equation
Markov Process Based Models
Concluding Summary
Problems
NONSTATIONARY MODELS
Some Applications
Envelope Function Model
Nonstationary Generalizations
Priestley's Model
SDoF Oscillator Response
Multi DoF Oscillator Response
Nonstationary and Nonlinear Oscillator
Concluding Summary
Problems
THE MONTE CARLO METHOD
Introduction
Random Number Generation
Joint Random Numbers
Error Estimates
Applications
Concluding Summary
Problems
FLUID INDUCED VIBRATION
Ocean Currents and Waves
Fluid Forces - In General
Examples
Available Numerical Codes
Index
Applications
Units
Organization of the Text
Problems
EVENTS AND PROBABILITY
Sets
Probability
Concluding Summary
Problems
RANDOM VARIABLE MODELS
Probability Distribution Function
Probability Density Function
Mathematical Expectation
Variance
USEFUL PROBABILITY DENSITIES
Two Random Variables
Concluding Summary
Problems
FUNCTIONS OF RANDOM VARIABLES
Exact Functions of One Variable
Functions of Two or More RVs
General Case
Approximate Analysis
Monte Carlo Method
Concluding Summary
Problems
The Standard Normal Table
RANDOM PROCESSES
Basic Random Process Descriptors
Ensemble Averaging
Stationarity
Derivatives of Stationary Processes
Fourier Series and Fourier Transforms
Harmonic Processes
Power Spectra
Fourier Representation of a Random Process
Borgman's Method of Frequency Discretization
Concluding Summary
Problems
SINGLE DEGREE OF FREEDOM DYNAMICS
Motivating Examples
Deterministic SDoF Vibration
SDoF: The Response to Random Loads
Response to Two Random Loads
Concluding Summary
Problems
MULTI DEGREE OF FREEDOM VIBRATION
Deterministic Vibration
Response to Random Loads
Periodic Structures
Inverse Vibration
Random Eigenvalues
Concluding Summary
Problems
CONTINUOUS SYSTEM VIBRATION
Deterministic Continuous Systems
Sturm-Liouville Eigenvalue Problem
Deterministic Vibration
Random Vibration of Continuous System
Beams with Complex Loading
Concluding Summary
Problems
RELIABILITY
Introduction
First Excursion Failure
Fatigue Life Prediction
Concluding Summary
Problems
NONLINEAR DYNAMIC MODELS
Examples of Nonlinear Vibration
Fundamental Nonlinear Equations
Statistical Equivalent Linearization
Perturbation Methods
The van der Pol Equation
Markov Process Based Models
Concluding Summary
Problems
NONSTATIONARY MODELS
Some Applications
Envelope Function Model
Nonstationary Generalizations
Priestley's Model
SDoF Oscillator Response
Multi DoF Oscillator Response
Nonstationary and Nonlinear Oscillator
Concluding Summary
Problems
THE MONTE CARLO METHOD
Introduction
Random Number Generation
Joint Random Numbers
Error Estimates
Applications
Concluding Summary
Problems
FLUID INDUCED VIBRATION
Ocean Currents and Waves
Fluid Forces - In General
Examples
Available Numerical Codes
Index