Experimentation, validation, and uncertainty analysis for engineers

✍ Scribed by Coleman, Hugh W.; Steele, W. Glenn

• Publisher: John Wiley & Sons
• Year: 2018
• Tongue: English
• Leaves: 376
• Edition: Fourth edition
• Category: Library

✦ Table of Contents

Content: Cover
Title Page
Copyright
Contents
Preface
Chapter 1: Experimentation, Errors, and Uncertainty
1-1 Experimentation
1-1.1 Why Is Experimentation Necessary?
1-1.2 Degree of Goodness and Uncertainty Analysis
1-1.3 Experimentation and Validation of Simulations
1-2 Experimental Approach
1-2.1 Questions to Be Considered
1-2.2 Phases of Experimental Program
1-3 Basic Concepts and Definitions
1-3.1 Errors and Uncertainties
1-3.2 Categorizing and Naming Errors and Uncertainties
1-3.3 Estimating Standard Uncertainties
1-3.4 Determining Combined Standard Uncertainties. 1-3.5 Elemental Systematic Errors and Effects of Calibration1-3.6 Expansion of Concept from ""Measurement Uncertainty"" to ""Experimental Uncertainty
1-3.7 Repetition and Replication
1-3.8 Associating a Percentage Coverage or Confidence with Uncertainty Estimates
1-4 Experimental Results Determined from a Data Reduction Equation Combining Multiple Measured Variables
1-5 Guides and Standards
1-5.1 Experimental Uncertainty Analysis
1-5.2 Validation of Simulations
1-6 A Note on Nomenclature
References
Problems. Chapter 2: Coverage and Confidence Intervals for an Individual Measured Variable2-1 Coverage Intervals from the Monte Carlo Method for a Single Measured Variable
2-2 Confidence Intervals from the Taylor Series Method for a Single Measured Variable, Only Random Errors Considered
2-2.1 Statistical Distributions
2-2.2 The Gaussian Distribution
2-2.3 Confidence Intervals in Gaussian Parent Populations
2-2.4 Confidence Intervals in Samples from Gaussian Parent Populations
2-2.5 Tolerance and Prediction Intervals in Samples from Gaussian Parent Populations. 2-2.6 Statistical Rejection of Outliers from a Sample Assumed from a Gaussian Parent Population2-3 Confidence Intervals from the Taylor Series Method for a Single Measured Variable: Random and Systematic Errors Considered
2-3.1 The Central Limit Theorem
2-3.2 Systematic Standard Uncertainty Estimation
2-3.3 The TSM Expanded Uncertainty of a Measured Variable
2-3.4 The TSM Large-Sample Expanded Uncertainty of a Measured Variable
2-4 Uncertainty of Uncertainty Estimates and Confidence Interval Limits for a Measured Variable
2-4.1 Uncertainty of Uncertainty Estimates. 2-4.2 Implications of the Uncertainty in Limits of High Confidence Uncertainty Intervals Used in Analysis and DesignReferences
Problems
Chapter 3: Uncertainty in a Result Determined from Multiple Variables
3-1 General Uncertainty Analysis vs. Detailed Uncertainty Analysis
3-2 Monte Carlo Method for Propagation of Uncertainties
3-2.1 Using the MCM in General Uncertainty Analysis
3-2.2 Using the MCM in Detailed Uncertainty Analysis
3-3 Taylor Series Method for Propagation of Uncertainties
3-3.1 General Uncertainty Analysis Using the Taylor Series Method (TSM).

✦ Subjects

Engineering -- Experiments.;Uncertainty.;TECHNOLOGY & ENGINEERING -- Engineering (General);TECHNOLOGY & ENGINEERING -- Reference.