A Bayes network approach to uncertainty quantification in hierarchically developed computational models
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Vanderbilt Univ., Nashville, TN (United States)
- Thomas-Paez Consulting, Durango, CO (United States)
Here, performance assessment of complex systems is ideally accomplished through system-level testing, but because they are expensive, such tests are seldom performed. On the other hand, for economic reasons, data from tests on individual components that are parts of complex systems are more readily available. The lack of system-level data leads to a need to build computational models of systems and use them for performance prediction in lieu of experiments. Because their complexity, models are sometimes built in a hierarchical manner, starting with simple components, progressing to collections of components, and finally, to the full system. Quantification of uncertainty in the predicted response of a system model is required in order to establish confidence in the representation of actual system behavior. This paper proposes a framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions. It is based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.). Because epistemic sources of uncertainty were shown to be secondary, in this application, aleatoric only uncertainty is included in the present uncertainty quantification. An example showing application of the techniques to uncertainty quantification of measures of response of a real, complex aerospace system is included.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1344496
- Report Number(s):
- SAND-2011-4666J; 596984
- Journal Information:
- International Journal for Uncertainty Quantification, Vol. 2, Issue 2; ISSN 2152-5080
- Publisher:
- Begell HouseCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design
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journal | January 2020 |
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