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Helps engineers and scientists assess and manage uncertainty at all stages of experimentation and validation of simulations Fully updated from its previous edition, Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition includes expanded coverage and new examples of applying the Monte Carlo Method (MCM) in performing uncertainty analyses. Presenting the current, internationally accepted methodology from ISO, ANSI, and ASME standards for propagating uncertainties using both the MCM and the Taylor Series Method (TSM), it provides a logical approach to experimentation…mehr
Helps engineers and scientists assess and manage uncertainty at all stages of experimentation and validation of simulations Fully updated from its previous edition, Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition includes expanded coverage and new examples of applying the Monte Carlo Method (MCM) in performing uncertainty analyses. Presenting the current, internationally accepted methodology from ISO, ANSI, and ASME standards for propagating uncertainties using both the MCM and the Taylor Series Method (TSM), it provides a logical approach to experimentation and validation through the application of uncertainty analysis in the planning, design, construction, debugging, execution, data analysis, and reporting phases of experimental and validation programs. It also illustrates how to use a spreadsheet approach to apply the MCM and the TSM, based on the authors' experience in applying uncertainty analysis in complex, large-scale testing of real engineering systems. Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition includes examples throughout, contains end of chapter problems, and is accompanied by the authors' website www.uncertainty-analysis.com. * Guides readers through all aspects of experimentation, validation, and uncertainty analysis * Emphasizes the use of the Monte Carlo Method in performing uncertainty analysis * Includes complete new examples throughout * Features workable problems at the end of chapters Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition is an ideal text and guide for researchers, engineers, and graduate and senior undergraduate students in engineering and science disciplines. Knowledge of the material in this Fourth Edition is a must for those involved in executing or managing experimental programs or validating models and simulations.
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HUGH W. COLEMAN, PHD, PE, is a Professor Emeritus of Mechanical and Aerospace Engineering at the University of Alabama in Huntsville, USA. W. GLENN STEELE, PHD, PE, is a Professor Emeritus of Mechanical Engineering at Mississippi State University, USA. 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 courses." Steele was also awarded the ASME Performance Test Codes Medal in 2014. They have served on technical committees and 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.
Inhaltsangabe
Preface xv
1 Experimentation, Errors, and Uncertainty 1
1-1 Experimentation, 2
1-1.1 Why Is Experimentation Necessary?, 2
1-1.2 Degree of Goodness and Uncertainty Analysis, 3
1-1.3 Experimentation and Validation of Simulations, 5
1-2 Experimental Approach, 6
1-2.1 Questions to Be Considered, 7
1-2.2 Phases of Experimental Program, 8
1-3 Basic Concepts and Definitions, 8
1-3.1 Errors and Uncertainties, 9
1-3.2 Categorizing and Naming Errors and Uncertainties, 13
1-3.3 Estimating Standard Uncertainties, 15
1-3.4 Determining Combined Standard Uncertainties, 16
1-3.5 Elemental Systematic Errors and Effects of Calibration, 18
1-3.6 Expansion of Concept from "Measurement Uncertainty" to "Experimental Uncertainty", 20
1-3.7 Repetition and Replication, 22
1-3.8 Associating a Percentage Coverage or Confidence with Uncertainty Estimates, 24
1-4 Experimental Results Determined from a Data Reduction Equation Combining Multiple Measured Variables, 25
1-5 Guides and Standards, 27
1-5.1 Experimental Uncertainty Analysis, 27
1-5.2 Validation of Simulations, 29
1-6 A Note on Nomenclature, 31
References, 31
Problems, 32
2 Coverage and Confidence Intervals for an Individual Measured Variable 33
2-1 Coverage Intervals from the Monte Carlo Method for a Single Measured Variable, 34
2-2 Confidence Intervals from the Taylor Series Method for a Single Measured Variable, Only Random Errors Considered, 35
2-2.1 Statistical Distributions, 35
2-2.2 The Gaussian Distribution, 36
2-2.3 Confidence Intervals in Gaussian Parent Populations, 42
2-2.4 Confidence Intervals in Samples from Gaussian Parent Populations, 43
2-2.5 Tolerance and Prediction Intervals in Samples from Gaussian Parent Populations, 48
2-2.6 Statistical Rejection of Outliers from a Sample Assumed from a Gaussian Parent Population, 51
2-3 Confidence Intervals from the Taylor Series Method for a Single Measured Variable: Random and Systematic Errors Considered, 55
2-3.1 The Central Limit Theorem, 55
2-3.2 Systematic Standard Uncertainty Estimation, 56
2-3.3 The TSM Expanded Uncertainty of a Measured Variable, 58
2-3.4 The TSM Large-Sample Expanded Uncertainty of a Measured Variable, 61
2-4 Uncertainty of Uncertainty Estimates and Confidence Interval Limits for a Measured Variable, 63
2-4.1 Uncertainty of Uncertainty Estimates, 63
2-4.2 Implications of the Uncertainty in Limits of High Confidence Uncertainty Intervals Used in Analysis and Design, 65
References, 67
Problems, 68
3 Uncertainty in a Result Determined from Multiple Variables 71
3-1 General Uncertainty Analysis vs. Detailed Uncertainty Analysis, 72
3-2 Monte Carlo Method for Propagation of Uncertainties, 73
3-2.1 Using the MCM in General Uncertainty Analysis, 73
3-2.2 Using the MCM in Detailed Uncertainty Analysis, 75
3-3 Taylor Series Method for Propagation of Uncertainties, 78
3-3.1 General Uncertainty Analysis Using the Taylor Series Method (TSM), 79
3-3.2 Detailed Uncertainty Analysis Using the Taylor Series Method (TSM), 80
3-4 Determining MCM Coverage Intervals and TSM Expanded Uncertainty, 82
3-4.1 MCM Coverage Intervals for a Result, 82
3-4.2 TSM Expanded Uncertainty of a Result, 85
3-5 General Uncertainty Analysis Using the TSM and MSM Approaches for a Rough-walled Pipe Flow Experiment, 87
