Haym Benaroya and Seon Mi Han,

**«Probability Models in Engineering and Science»**

CRC | ISBN 0824723155 | June 24, 2005 | PDF | 10,3 Mb | 760 Pages

Thanks to Eusoof for the original upload!!

Probabilistic Models in Engineering and Science provides engineers, scientists, and students with a self-contained, comprehensive introduction to applied probabilistic modeling. Perfectly suited to undergraduate and graduate coursework, professional reference, or self-study, this book develops applied probability along with the historical context of the field, providing short biographies and portraits of key "names" mentioned in the book. The authors have included extensive example problems, ample end-of-chapter problems, and footnotes that provide references for further in-depth information. Topics include random processes, reliability, and the Monte Carlo method.

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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.

# Offers an easy-to-follow approach that makes complex concepts accessible and relevant

# Includes a wealth of example problems, some that work to explain concepts and others that integrate several concepts to show how the theory works as a whole

# Contains many end-of-chapter exercises that reinforce concepts

# Devotes a chapter to fluid induced vibration that demonstrates the application of probabilistic models to offshore structural engineering

:::::::::::TABLE OF CONTENTS::::::::::::

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