Dakota-MCNP Coupling for Uncertainty Analysis using Polynomial Chaos Expansions
- Oregon State University, 1500 SW Jefferson St., Corvallis, OR 97331 (United States)
Uncertainty quantification (UQ) seeks to characterize the total uncertainty in a system's response given the cumulative effect of uncertainties found in the physical input parameters. UQ is closely related to, and is often followed by, sensitivity analysis (SA). SA seeks to characterize the contribution individual uncertain parameters have on the total uncertainty in the system. UQ and SA are important, because it is often difficult to establish confidence levels in numerical predictions. This arises due to the differences between noumenal reality and computer modeled simulations. Often, these are due either to a lack of knowledge about the problem being analyzed (epistemic uncertainty), or impracticality associated with modeling and simulating a physical phenomenon sufficiently accurately. It is the primary goal of UQ and SA to first quantify the difference between reality and simulation, and second to determine the significance this difference has on relevant physical responses.
- OSTI ID:
- 23047494
- Journal Information:
- Transactions of the American Nuclear Society, Vol. 116; Conference: 2017 Annual Meeting of the American Nuclear Society, San Francisco, CA (United States), 11-15 Jun 2017; Other Information: Country of input: France; 9 refs.; available from American Nuclear Society - ANS, 555 North Kensington Avenue, La Grange Park, IL 60526 (US); ISSN 0003-018X
- Country of Publication:
- United States
- Language:
- English
Similar Records
Quantifying the Uncertainty in Deterministic Phonon Transport Calculations of Thermal Conductivity using Polynomial Chaos Expansions
Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions