Experimentation, Validation, and Uncertainty Analysis for Engineers

ISBN-10: 0470168889
ISBN-13: 9780470168882
Edition: 3rd 2009
List price: $214.95 Buy it from $27.68 Rent it from $21.63
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Description: Experimentation, Validation, and Uncertainty Analysis for Engineers, Third Edition provides a thorough description of techniques for greater sophistication and verifiability to engineering experiments from early stages through debugging, execution,  More...

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Book details

List price: $214.95
Edition: 3rd
Copyright year: 2009
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/13/2009
Binding: Hardcover
Pages: 336
Size: 6.25" wide x 9.50" long x 0.75" tall
Weight: 1.232
Language: English

Experimentation, Validation, and Uncertainty Analysis for Engineers, Third Edition provides a thorough description of techniques for greater sophistication and verifiability to engineering experiments from early stages through debugging, execution, data analysis, and reporting phases. New material includes direct Monte Carlo (MC) simulation, incorporation of the new approach to determining the random uncertainty of a result in steady state testing, plus a new chapter on ldquo;Verification and Validation of Simulation Results.rdquo; Practicing engineers (Mechanical, Chemical, Electrical, Materials, Industrial), as well as engineering students in upper-level undergraduate and graduate curriculums, will benefit greatly from this valuable source.

#60;p#62;Hugh W. Coleman, PhD, PE, is a Professor of Mechanical and Aerospace Engineering at the University of Alabama in Huntsville. W. GLENN STEELE, PhD, PE, is a Giles Distinguished Professor and the Bobby Shackouls Professor of Mechanical Engineering at Mississippi State University.#60;/p#62;#60;p#62;Coleman and Steele received the prestigious AIAA Ground Testing Award for "pioneering efforts in experimental uncertainty analysis with significant methodology advances and effective dissemination of knowledge through a straightforward engineering approach in their text and short course." They have served on experimental uncertainty and validation standards committees associated with ASME, AIAA, SAE, ISO, and NATO AGARD. They are both Fellows of ASME and Associate Fellows of AIAA.

Preface
Experimentation, Errors, and Uncertainty
Experimentation
Why Is Experimentation Necessary?
Degree of Goodness and Uncertainty Analysis
Experimentation and Validation of Simulations
Experimental Approach
Questions to Be Considered
Phases of Experimental Program
Basic Concepts and Definitions
Errors and Uncertainties
Degree of Confidence and Uncertainty Intervals
Expansion of Concept from "Measurement Uncertainty" to "Experimental Uncertainty,"
Elemental Systematic Errors and Effects of Calibration
Repetition and Replication
Experimental Results Determined from Multiple Measured Variables
Guides and Standards
Experimental Uncertainty Analysis
Validation of Simulations
A Note on Nomenclature
References
Problems
Errors and Uncertainties in a Measured Variable
Statistical Distributions
Gaussian Distribution
Mathematical Description
Confidence Intervals in Gaussian Distribution
Samples from Gaussian Parent Population
Statistical Parameters of Sample Population
Confidence Intervals in Sample Populations
Tolerance and Prediction Intervals in Sample Populations
Statistical Rejection of Outliners from a Sample
Uncertainty of a Measured Variable
Systematic Standard Uncertainty Estimation
Overall Uncertainty of a Measured Variable
Large-Sample Uncertainty of a Measured Variable
Uncertainty of Measured Variable by Monte Carlo Method
Summary
References
Problems
Uncertainty in a Result Determined from Multiple Variables
Taylor Series Method for Propagation of Uncertainties
TSM for Function of Multiple Variables
Expanded Uncertainty of a Result
Large-Sample Approximation for Uncertainty of a Result
Example of TSM Uncertainty Propagation
Numerical Approximation for TSM Propagation of Uncertainties
Monte Carlo Method for Propagation of Uncertainties
General Approach for MCM
Example of MCM Uncertainty Propagation
Coverage Intervals for MCM Simulations
Example of Determination of MCM Coverage Interval
References
Problems
General Unvertainty Analysis: Planning an Experiment and Application in Validation
Overview: Using Uncertainty Propagation in Experiments and Validation
Application in Experimentation
General Uncertainty Analysis Using the Taylor Series Method (TSM)
Application to Experiment Planning (TSM)
Simple Case
Special Functional Form
Using TSM Uncertainty Analysis in Planning an Experiment
Example: Analysis of Proposed Particulate Measuring System
The Problem
Proposed Measurement Technique and System
Analysis of Proposed Experiment
Implications of Uncertainty Analysis Results
Design Changes Indicated by Uncertainty Analysis
Example: Analysis of Proposed Heat Transfer Experiment
The Problem
Two Proposed Experimental Techniques
General Uncertainty Analysis: Steady-State Technique
General Uncertainty Analysis: Transient Technique
Implications of Uncertainty Analysis Results
Examples of Presentation of Results from Actual Applications
Results from Analysis of a Turbine Test
Results from Analysis of a Solar Thermal Absorber/Thruster Test
Application in Validation: Estimating Uncertainty in Simulation Result due to Uncertainties in Inputs
References
Problems
Detailed Uncertainty Analysis: Designing, Debugging, and Executing an Experiment
Using Detailed Uncertainty Analysis
Detailed Uncertainty Analysis: Overview of Complete Methodology
Determining Random Uncertainty of Experimental Result
Example: Random Uncertainty Determination in Compressible Flow Venturi Meter Calibration Facility
Example: Random Uncertainty Determination in Laboratory-Scale Ambient Temperature Flow Test Facility
Example: Random Uncertainty Determination in Full-Scale Rocket Engine Ground Test Facility
Summary
Determining Systematic Uncertainty of Experimental Result
Systematic Uncertainty for Single Variable
Some Practical Considerations
Digital Data Acquisition Errors
Property Value Uncertainty
Systematic Uncertainty of a Result Including Correlated Systematic Error Effects
Example: Correlated Errors in a Temperature Difference
Example: Correlated Errors in an Average Velocity
Comparative Testing and Correlated Systematic Error Effects
Result Is a Difference of Test Results
Result Is a Ratio of Test Results
Comprehensive Example: Sample-to-Sample Experiment
Problem
Measurement System
Zeroth-Order Replication-Level Analysis
First-Order Replication-Level Analysis
Nth-Order Replication-Level Analysis
Comprehensive Example: Debugging and Qualification of a Timewise Experiment
Basic Ideas
Example
Some Additional Considerations in Experiment Execution
Choice of Test Points: Rectification
Example of Use of Rectification
Choice of Test Sequence
Relationship to Statistical Design of Experiments
Use of Balance Checks
Application to a Flow System
Use of a Jitter Program
Comments on Transient Testing
References
Problems
Validation Of Simulations
Introduction to Validation Methodology
Errors and Uncertainties
Validation Nomenclature
Validation Approach
Code and Solution Verification
Estimation of Validation Uncertainty uval
Estimating uval When Experimental Value D of Validation Variable Is Directly Measured (Case 1)
Estimating uval When Experimental Value D of Validation Variable Is Determined from Data Reduction Equation (Case 2 and 3)
No Measured Variables Share Identical Error Sources (Case 2)
Measured Variables Share Identical Error Sources (Case 3)
Estimating Uval When Experimental Value D of Validation Variable Is Determined from Data Reduction Equation That Itself Is a Model (Case 4)
Interpretation of Validation Results Using E and uval
Interpretation with No Assumptions Made about Error Distributions
Interpretation with Assumptions Made about Error Distributions
Some Practical Points
References
Data Analysis, Regression, and Reporting of Results
Overview of Regression Analysis and Its Uncertainty
Categories of Regression Uncertainty
Uncertainty in Coefficients
Uncertainty in Y From Regression Model
(Xi, Yi) Variables Are Functions
Least-Squares Estimation
Classical Linear Regression Uncertainty: Random Uncertainty
Comprehensive Approach to Linear Regression Uncertainty
Uncertainty in Coefficients: First-Order Regression
Uncertainty in Y from Regression Model: First-Order Regression
Higher Order Regressions
Reporting Regression Uncertainties
Regressions in Which X and Y Are Functional Relations
Examples of Determining Regressions and Their Uncertainties
Experimental Apparatus
Pressure Transducer Calibration and Uncertainty
Venturi Discharge Coefficient and Its Uncertainty
Flow Rate and Its Uncertainty in a Test
Multiple Linear Regression
References
Problems
Useful Statistics
Taylor Series Method (TSM) for Uncertainty Propagation
Derivation of Uncertainty Propagation Equation
Comparison with Previous Approaches
Abernethy et al. Approach
Coleman and Steele Approach
ISO Guide Approach
AIAA Standard [11], AGARD [12], and ANSI/ASME [13] Approach
NIST Approach
Additional Assumptions for Engineering Applications
Approximating the Coverage Factor
References
Comparison of Models for Calculation of Uncertainty
Monte Carlo Simulations
Simulation Results
References
Shortest Coverage Interval for Monte Carlo Method
Reference
Asymmetric Systematic Uncertainties
Procedure for Asymmetric Systematic Uncertainties Using TSM Propagation
Procedure for Asymmetric Systematic Uncertainties Using MCM Propagation
Example: Biases in a Gas Temperature Measurement System
References
Dynamic Response of Instrument Systems
General Instrument Response
Response of Zero-Order Instruments
Response of First-Order Instruments
Response of Second-Order Instruments
Summary
References
Index

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