Experimentation, Validation, and Uncertainty Analysis for Engineers, 4th Edition

Experimentation, Validation, and Uncertainty Analysis for Engineers, 4th Edition

by Hugh W. Coleman, W. Glenn Steele
Released May 2018
Publisher(s): Wiley
ISBN: 9781119417514

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Book description

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.

Table of contents

  1. COVER
  2. TITLE PAGE
  3. PREFACE
  4. 1 EXPERIMENTATION, ERRORS, AND UNCERTAINTY
    1. 1-1 EXPERIMENTATION
    2. 1-2 EXPERIMENTAL APPROACH
    3. 1-3 BASIC CONCEPTS AND DEFINITIONS
    4. 1-4 EXPERIMENTAL RESULTS DETERMINED FROM A DATA REDUCTION EQUATION COMBINING MULTIPLE MEASURED VARIABLES
    5. 1-5 GUIDES AND STANDARDS
    6. 1-6 A NOTE ON NOMENCLATURE
    7. REFERENCES
    8. PROBLEMS
    9. NOTES
  5. 2 COVERAGE AND CONFIDENCE INTERVALS FOR AN INDIVIDUAL MEASURED VARIABLE
    1. 2-1 COVERAGE INTERVALS FROM THE MONTE CARLO METHOD FOR A SINGLE MEASURED VARIABLE
    2. 2-2 CONFIDENCE INTERVALS FROM THE TAYLOR SERIES METHOD FOR A SINGLE MEASURED VARIABLE, ONLY RANDOM ERRORS CONSIDERED
    3. 2-3 CONFIDENCE INTERVALS FROM THE TAYLOR SERIES METHOD FOR A SINGLE MEASURED VARIABLE: RANDOM AND SYSTEMATIC ERRORS CONSIDERED
    4. 2-4 UNCERTAINTY OF UNCERTAINTY ESTIMATES AND CONFIDENCE INTERVAL LIMITS FOR A MEASURED VARIABLE
    5. REFERENCES
    6. PROBLEMS
    7. NOTES
  6. 3 UNCERTAINTY IN A RESULT DETERMINED FROM MULTIPLE VARIABLES
    1. 3-1 GENERAL UNCERTAINTY ANALYSIS VS. DETAILED UNCERTAINTY ANALYSIS
    2. 3-2 MONTE CARLO METHOD FOR PROPAGATION OF UNCERTAINTIES
    3. 3-3 TAYLOR SERIES METHOD FOR PROPAGATION OF UNCERTAINTIES
    4. 3-4 DETERMINING MCM COVERAGE INTERVALS AND TSM EXPANDED UNCERTAINTY
    5. 3-5 GENERAL UNCERTAINTY ANALYSIS USING THE TSM AND MSM APPROACHES FOR A ROUGH-WALLED PIPE FLOW EXPERIMENT
    6. 3-6 COMMENTS ON IMPLEMENTING DETAILED UNCERTAINTY ANALYSIS USING A SPREADSHEET
    7. REFERENCES
    8. PROBLEMS
  7. 4 GENERAL UNCERTAINTY ANALYSIS USING THE TAYLOR SERIES METHOD (TSM)
    1. 4-1 TSM APPLICATION TO EXPERIMENT PLANNING
    2. 4-2 TSM APPLICATION TO EXPERIMENT PLANNING: SPECIAL FUNCTIONAL FORM
    3. 4-3 USING TSM UNCERTAINTY ANALYSIS IN PLANNING AN EXPERIMENT
    4. 4-4 EXAMPLE: ANALYSIS OF PROPOSED PARTICULATE MEASURING SYSTEM
    5. 4-5 EXAMPLE: ANALYSIS OF PROPOSED HEAT TRANSFER EXPERIMENT
    6. 4-6 EXAMPLES OF PRESENTATION OF RESULTS FROM ACTUAL APPLICATIONS
    7. REFERENCES
    8. PROBLEMS
  8. 5 DETAILED UNCERTAINTY ANALYSIS: OVERVIEW AND DETERMINING RANDOM UNCERTAINTIES IN RESULTS
    1. 5-1 USING DETAILED UNCERTAINTY ANALYSIS
    2. 5-2 DETAILED UNCERTAINTY ANALYSIS: OVERVIEW OF COMPLETE METHODOLOGY
    3. 5-3 DETERMINING RANDOM UNCERTAINTY OF EXPERIMENTAL RESULT
    4. REFERENCES
  9. 6 DETAILED UNCERTAINTY ANALYSIS: DETERMINING SYSTEMATIC UNCERTAINTIES IN RESULTS
    1. 6-1 ESTIMATING SYSTEMATIC UNCERTAINTIES
    2. 6-2 DETERMINING SYSTEMATIC UNCERTAINTY OF EXPERIMENTAL RESULT INCLUDING CORRELATED SYSTEMATIC ERROR EFFECTS
    3. 6-3 COMPARATIVE TESTING
    4. 6-4 SOME ADDITIONAL CONSIDERATIONS IN EXPERIMENT EXECUTION
    5. REFERENCES
    6. PROBLEMS
  10. 7 DETAILED UNCERTAINTY ANALYSIS: COMPREHENSIVE EXAMPLES
    1. 7-1 TSM COMPREHENSIVE EXAMPLE: SAMPLE-TO-SAMPLE EXPERIMENT
    2. 7-2 TSM COMPREHENSIVE EXAMPLE: USE OF BALANCE CHECKS
    3. 7-3 COMPREHENSIVE EXAMPLE: DEBUGGING AND QUALIFICATION OF A TIMEWISE EXPERIMENT
    4. 7-4 COMPREHENSIVE EXAMPLE: HEAT EXCHANGER TEST FACILITY FOR SINGLE AND COMPARATIVE TESTS
    5. 7-5 CASE STUDY: EXAMPLES OF SINGLE AND COMPARATIVE TESTS OF NUCLEAR POWER PLANT RESIDUAL HEAT REMOVAL HEAT EXCHANGER
    6. REFERENCES
    7. PROBLEMS
  11. 8 THE UNCERTAINTY ASSOCIATED WITH THE USE OF REGRESSIONS
    1. 8-1 OVERVIEW OF LINEAR REGRESSION ANALYSIS AND ITS UNCERTAINTY
    2. 8-2 DETERMINING AND REPORTING REGRESSION UNCERTAINTY
    3. 8-3 METHOD OF LEAST SQUARES REGRESSION
    4. 8-4 FIRST-ORDER REGRESSION EXAMPLE: MCM APPROACH TO DETERMINE REGRESSION UNCERTAINTY
    5. 8-5 REGRESSION EXAMPLES: TSM APPROACH TO DETERMINE REGRESSION UNCERTAINTY
    6. 8-6 COMPREHENSIVE TSM EXAMPLE: REGRESSIONS AND THEIR UNCERTAINTIES IN A FLOW TEST
    7. REFERENCES
    8. PROBLEMS
    9. NOTES
  12. 9 VALIDATION OF SIMULATIONS
    1. 9-1 INTRODUCTION TO VALIDATION METHODOLOGY
    2. 9-2 ERRORS AND UNCERTAINTIES
    3. 9-3 VALIDATION NOMENCLATURE
    4. 9-4 VALIDATION APPROACH
    5. 9-5 CODE AND SOLUTION VERIFICATION
    6. 9-6 INTERPRETATION OF VALIDATION RESULTS USING E AND uval
    7. 9-7 ESTIMATION OF VALIDATION UNCERTAINTY uval
    8. 9-8 SOME PRACTICAL POINTS
    9. REFERENCES
  13. ANSWERS TO SELECTED PROBLEMS
  14. APPENDIX A: USEFUL STATISTICS
  15. APPENDIX B: TAYLOR SERIES METHOD (TSM) FOR UNCERTAINTY PROPAGATION
    1. B-1 DERIVATION OF UNCERTAINTY PROPAGATION EQUATION
    2. B-2 COMPARISON WITH PREVIOUS APPROACHES
    3. B-3 ADDITIONAL ASSUMPTIONS FOR ENGINEERING APPLICATIONS
    4. REFERENCES
    5. NOTE
  16. APPENDIX C: COMPARISON OF MODELS FOR CALCULATION OF UNCERTAINTY
    1. C-1 MONTE CARLO SIMULATIONS
    2. C-2 SIMULATION RESULTS
    3. REFERENCES
  17. APPENDIX D: SHORTEST COVERAGE INTERVAL FOR MONTE CARLO METHOD
    1. REFERENCE
  18. APPENDIX E: ASYMMETRIC SYSTEMATIC UNCERTAINTIES
    1. E-1 PROCEDURE FOR ASYMMETRIC SYSTEMATIC UNCERTAINTIES USING TSM PROPAGATION
    2. E-2 PROCEDURE FOR ASYMMETRIC SYSTEMATIC UNCERTAINTIES USING MCM PROPAGATION
    3. E-3 EXAMPLE: BIASES IN A GAS TEMPERATURE MEASUREMENT SYSTEM
    4. REFERENCES
  19. APPENDIX F: DYNAMIC RESPONSE OF INSTRUMENT SYSTEMS
    1. F-1 GENERAL INSTRUMENT RESPONSE
    2. F-2 RESPONSE OF ZERO-ORDER INSTRUMENTS
    3. F-3 RESPONSE OF FIRST-ORDER INSTRUMENTS
    4. F-4 RESPONSE OF SECOND-ORDER INSTRUMENTS
    5. F-5 SUMMARY
    6. REFERENCES
  20. INDEX
  21. END USER LICENSE AGREEMENT

Product information

  • Title: Experimentation, Validation, and Uncertainty Analysis for Engineers, 4th Edition
  • Author(s): Hugh W. Coleman, W. Glenn Steele
  • Release date: May 2018
  • Publisher(s): Wiley
  • ISBN: 9781119417514