Polynomial regression approaches using derivative information for uncertainty quantification.
In this work we describe a polynomial regression approach that uses derivative information for analyzing the performance of a complex system that is described by a mathematical model depending on several stochastic parameters. We construct a surrogate model as a goal-oriented projection onto an incomplete space of polynomials; find coordinates of the projection by regression; and use derivative information to significantly reduce the number of the sample points required to obtain a good model. The simplified model can be used as a control variate to significantly reduce the sample variance of the estimate of the goal. For our test model, we take a steady-state description of heat distribution in the core of the nuclear reactor core, and as our goal we take the maximum centerline temperature in a fuel pin. For this case, the resulting surrogate model is substantially more computationally efficient than random sampling or approaches that do not use derivative information, and it has greater precision than linear models.
- Research Organization:
- Argonne National Laboratory (ANL)
- Sponsoring Organization:
- SC
- DOE Contract Number:
- AC02-06CH11357
- OSTI ID:
- 972621
- Report Number(s):
- ANL/MCS/JA-64224
- Journal Information:
- Nucl. Sci. Eng., Journal Name: Nucl. Sci. Eng. Journal Issue: 2 ; Feb. 2010 Vol. 164; ISSN NSENAO; ISSN 0029-5639
- Country of Publication:
- United States
- Language:
- ENGLISH
Similar Records
Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation
Dimensionality reduction for uncertainty quantification of nuclear engineering models.
S&T Accomplishment Report
·
Sat Jun 01 00:00:00 EDT 2024
·
OSTI ID:2467452
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation
Journal Article
·
Tue Sep 01 00:00:00 EDT 2020
· Journal of Computational Physics
·
OSTI ID:1639155
Dimensionality reduction for uncertainty quantification of nuclear engineering models.
Journal Article
·
Fri Dec 31 23:00:00 EST 2010
· Trans. Am. Nucl. Soc.
·
OSTI ID:1014845