Experimentation, validation, and uncertainty analysis for engineers
Containing end-of-chapter problems and examples throughout, this must-read guide helps engineers and scientists assess and manage uncertainty at all stages of experimentation and validation of simulations. --
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ, USA :
John Wiley & Sons, Inc
2018.
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| Edición: | 4th ed |
| Colección: | Wiley ebooks.
Engineering professional collection. |
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b40628073*spi |
Tabla de Contenidos:
- 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).
