## Verification and Validation in Scientific ComputingAdvances in scientific computing have made modelling and simulation an important part of the decision-making process in engineering, science, and public policy. This book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification and validation of models and simulations. The emphasis is placed on models that are described by partial differential and integral equations and the simulations that result from their numerical solution. The methods described can be applied to a wide range of technical fields, from the physical sciences, engineering and technology and industry, through to environmental regulations and safety, product and plant safety, financial investing, and governmental regulations. This book will be genuinely welcomed by researchers, practitioners, and decision makers in a broad range of fields, who seek to improve the credibility and reliability of simulation results. It will also be appropriate either for university courses or for independent study. |

### What people are saying - Write a review

User Review - Flag as inappropriate

Scientific computing is finding more uses in engineering and research. This book is about model verification. The questions are how well a simulation matches an actual activity, or how to get experimental data for a mathematics of micro- and nano-scales, and whether reviewers will find the results credible. Verification activities are shown for software, solution, model and management. Predictive capability is summarized in several steps for identifying sources of uncertainty, characterizing them, estimating error and uncertainty in the system response quantities (SRQs), updating the model, and analyzing sensitivities. There are five parts for sixteen chapters, and an appendix.

### Other editions - View all

Verification and Validation in Scientific Computing William L. Oberkampf,Christopher J. Roy No preview available - 2010 |

### Common terms and phrases

AIAA aleatory uncertainty algorithm analysis angle of attack application approach benchmark boundary conditions calibration Chapter characterization code verification complex computational fluid dynamics computational results conducted confidence interval considered coupled physics differential equations discrete equations discretization error discussed engineering epistemic uncertainty evaluated exact solution example experimental data experimental measurements Ferson finite element flowfield fluid dynamics formal order function geometry grid input quantities iterative convergence iterative error L2 norm linear manufactured solution mathematical model mesh refinement methods model uncertainty model validation nondeterministic Oberkampf observed order order of accuracy order verification p-box parameters PCMM PDEs problem procedure random range regression reliability requirements residual Richardson extrapolation samples Sandia National Laboratories scientific computing solution verification spatial specific SRQs of interest structure system of interest system response quantities temperature Trucano truncation error u-values validation domain validation experiments variables verification and validation