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EPR 2013 Abstracts Abstract Details
| Presentation: | submitted: | by: |
|---|---|---|
| oberkampf.pdf | 2013-02-14 17:52:44 | William Oberkampf |
Concepts and Practice of Verification, Validation, and Uncertainty Quantification
Author: William L. Oberkampf
Requested Type: Consider for Invited
Submitted: 2013-01-14 19:39:45
Co-authors:
Contact Info:
W. L. Oberkampf Consulting
5112 Hidden Springs Trail
Georgetown, Texas 78633
USA
Abstract Text:
Verification and validation (V&V) are the primary means to assess the numerical and physics modeling accuracy, respectively, in computational simulation. Code verification deals with the assessment of the reliability of the software coding and the numerical algorithms used in obtaining a solution, while solution verification deals with numerical error estimation of the computational solution of a mathematical model for a specified set of initial and boundary conditions. Validation assesses the accuracy of the mathematical model as compared to experimentally measured quantities of the system of interest. Uncertainty quantification (UQ) in predictive capability attempts to characterize and estimate the uncertainties due to: the assumptions and approximations in the formulation of the mathematical model, the error incurred in the numerical solution of the discretized model, and the experimental measurements of input and output data. This presentation will briefly discuss the principles and practices of VVUQ from both the perspective of computational modeling and conducting high quality validation experiments. Contrasts will be drawn between weak and strong code verification testing, and model validation as opposed to model calibration. Research issues in predictive uncertainty estimation will be mentioned with regard to extrapolation of the model to conditions for which no experimental data are available.
Characterization: 2.0
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