Verification and Validation in Scientific Computing 1st Edition by William Oberkampf ISBN B010WEVST0 9781139813761

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ISBN 10: B010WEVST0
ISBN 13: 9781139813761
Author:  William Oberkampf

Verification and Validation in Scientific Computing 1st  Table of contents:

1 Introduction

1.1 Historical and modern role of modeling and simulation

1.2 Credibility of scientific computing

1.3 Outline and use of the book

1.4 References

Part I Fundamental concepts

2 Fundamental concepts and terminology

2.1 Development of concepts and terminology

2.2 Primary terms and concepts

2.3 Types and sources of uncertainties

2.4 Error in a quantity

2.5 Integration of verification, validation, and prediction

2.6 References

3 Modeling and computational simulation

3.1 Fundamentals of system specifications

3.2 Fundamentals of models and simulations

3.3 Risk and failure

3.4 Phases of computational simulation

3.5 Example problem: missile flight dynamics

3.6 References

Part II Code verification

4 Software engineering

4.1 Software development

4.2 Version control

4.3 Software verification and validation

4.4 Software quality and reliability

4.5 Case study in reliability: the T experiments

4.6 Software engineering for large software projects

4.7 References

5 Code verification

5.1 Code verification criteria

5.2 Definitions

5.3 Order of accuracy

5.4 Systematic mesh refinement

5.5 Order verification procedures

5.6 Responsibility for code verification

5.7 References

6 Exact solutions

6.1 Introduction to differential equations

6.2 Traditional exact solutions

6.3 Method of manufactured solutions (MMS)

6.4 Physically realistic manufactured solutions

6.5 Approximate solution methods

6.6 References

Part III Solution verification

7 Solution verification

7.1 Elements of solution verification

7.2 Round-off error

7.3 Statistical sampling error

7.4 Iterative error

7.5 Numerical error versus numerical uncertainty

7.6 References

8 Discretization error

8.1 Elements of the discretization process

8.2 Approaches for estimating discretization error

8.3 Richardson extrapolation

8.4 Reliability of discretization error estimators

8.5 Discretization error and uncertainty

8.6 Roache’s grid convergence index (GCI)

8.7 Mesh refinement issues

8.8 Open research issues

8.9 References

9 Solution adaptation

9.1 Factors affecting the discretization error

9.2 Adaptation criteria

9.3 Adaptation approaches

9.4 Comparison of methods for driving mesh adaptation

9.5 References

Part IV Model validation and prediction

10 Model validation fundamentals

10.1 Philosophy of validation experiments

10.2 Validation experiment hierarchy

10.3 Example problem: hypersonic cruise missile

10.4 Conceptual, technical, and practical difficulties of validation

10.5 References

11 Design and execution of validation experiments

11.1 Guidelines for validation experiments

11.2 Validation experiment example: Joint Computational/Experimental Aerodynamics Program (JCEAP)

11.3 Example of estimation of experimental measurement uncertainties in JCEAP

11.4 Example of further computational–experimental synergism in JCEAP

11.5 References

12 Model accuracy assessment

12.1 Elements of model accuracy assessment

12.2 Approaches to parameter estimation and validation metrics

12.3 Recommended features for validation metrics

12.4 Introduction to the approach for comparing means

12.5 Comparison of means using interpolation of experimental data

12.6 Comparison of means requiring linear regression of the experimental data

12.7 Comparison of means requiring nonlinear regression of the experimental data

12.8 Validation metric for comparing p-boxes

12.9 References

13 Predictive capability

13.1 Step 1: identify all relevant sources of uncertainty

13.2 Step 2: characterize each source of uncertainty

13.3 Step 3: estimate numerical solution error

13.4 Step 4: estimate output uncertainty

13.5 Step 5: conduct model updating

13.6 Step 6: conduct sensitivity analysis

13.7 Example problem: thermal heating of a safety component

13.8 Bayesian approach as opposed to PBA

13.9 References

Part V Planning, management, and implementation issues

14 Planning and prioritization in modeling and simulation

14.1 Methodology for planning and prioritization

14.2 Phenomena identification and ranking table (PIRT)

14.3 Gap analysis process

14.4 Planning and prioritization with commercial codes

14.5 Example problem: aircraft fire spread during crash landing

14.6 References

15 Maturity assessment of modeling and simulation

15.1 Survey of maturity assessment procedures

15.2 Predictive capability maturity model

15.3 Additional uses of the PCMM

15.4 References

16 Development and responsibilities for verification, validation and uncertainty quantification

16.1 Needed technical developments

16.2 Staff responsibilities

16.3 Management actions and responsibilities

16.4 Development of databases

16.5 Development of standards

16.6 References

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