Book description
This book places particular emphasis on issues of model quality and ideas of model testing and validation. Mathematical and computer-based models provide a foundation for explaining complex behaviour, decision-making, engineering design and for real-time simulators for research and training. Many engineering design techniques depend on suitable models, assessment of the adequacy of a given model for an intended application is therefore critically important. Generic model structures and dependable libraries of sub-models that can be applied repeatedly are increasingly important. Applications are drawn from the fields of mechanical, aeronautical and control engineering, and involve non-linear lumped-parameter models described by ordinary differential equations.- Focuses on issues of model quality and the suitability of a given model for a specific application
- Multidisciplinary problems within engineering feature strongly in the applications
- The development and testing of nonlinear dynamic models is given very strong emphasis
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- List of figures
- List of tables
- List of abbreviations
- Acknowledgements
- Preface
- About the author
- Chapter 1: The principles of system modelling
- Chapter 2: Integrated systems and their significance for system modelling
- Chapter 3: Problem organisation
- Chapter 4: Inverse simulation for system modelling and design
-
Chapter 5: Methods and applications of parameter sensitivity analysis
- Abstract:
- 5.1 Fundamental concepts of parameter sensitivity analysis
- 5.2 The sensitivity function
- 5.3 Methods of sensitivity analysis involving repeated solutions
- 5.4 Methods of sensitivity analysis involving sensitivity models
- 5.5 Case study: sensitivity analysis applied to the unmanned underwater vehicle (UUV) model
- 5.6 Sensitivity analysis using bond graphs
- 5.7 Sensitivity analysis in inverse simulation
-
Chapter 6: Experimental modelling: system identification, parameter estimation and model optimisation techniques
- Abstract:
- 6.1 The use of system identification and optimisation techniques in the development of physically based dynamic models
- 6.2 Applications of conventional methods of system identification and parameter estimation to physically based models
- 6.3 System identification and parameter estimation applied to helicopter flight mechanics models
- 6.4 Some selected methods of local and global parameter optimisation
- 6.5 Genetic programming (GP) for model structure estimation
- 6.6 Some practical issues in global parameter optimisation
- 6.7 Further examples of system identification, parameter estimation and model optimisation techniques in integrated systems applications
-
Chapter 7: Issues of model quality and the validation of dynamic models
- Abstract:
- 7.1 An introduction to the issues of model quality and validation
- 7.2 Model quality concepts, model uncertainties and modelling errors
- 7.3 Model testing, verification and validation
- 7.4 Issues of model validation and model quality in typical applications
- 7.5 Issues of model quality in model reduction
- 7.6 Discussion
- Chapter 8: Real-time simulation, virtual prototyping and partial-system testing
- Chapter 9: Model management
- Chapter 10: Further discussion
- Appendix A1: models of an unmanned underwater vehicle (UUV)
- Appendix A2: numerical methods for the solution of ordinary differential equations
- Index
Product information
- Title: Modelling and Simulation of Integrated Systems in Engineering
- Author(s):
- Release date: May 2012
- Publisher(s): Woodhead Publishing
- ISBN: 9780857096050
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