Statistical modeling support for calibration of a multiphysics model of subcooled boiling flows
Abstract
Nuclear reactor system analyses rely on multiple complex models which describe the physics of reactor neutronics, thermal hydraulics, structural mechanics, coolant physico-chemistry, etc. Such coupled multiphysics models require extensive calibration and validation before they can be used in practical system safety study and/or design/technology optimization. This paper presents an application of statistical modeling and Bayesian inference in calibrating an example multiphysics model of subcooled boiling flows which is widely used in reactor thermal hydraulic analysis. The presence of complex coupling of physics in such a model together with the large number of model inputs, parameters and multidimensional outputs poses significant challenge to the model calibration method. However, the method proposed in this work is shown to be able to overcome these difficulties while allowing data (observation) uncertainty and model inadequacy to be taken into consideration. (authors)
- Authors:
-
- Idaho National Laboratory, MS-3870, PO Box 1625, Idaho Falls, ID, 83415 (United States)
- Los Alamos National Laboratory, MS-F600, PO Box 1663, Los Alamos, NM, 87545 (United States)
- Publication Date:
- Research Org.:
- American Nuclear Society, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
- OSTI Identifier:
- 22212871
- Resource Type:
- Conference
- Resource Relation:
- Conference: M and C 2013: 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, Sun Valley, ID (United States), 5-9 May 2013; Other Information: Country of input: France; 28 refs.; Related Information: In: Proceedings of the 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering - M and C 2013| 3016 p.
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 22 GENERAL STUDIES OF NUCLEAR REACTORS; 97 MATHEMATICAL METHODS AND COMPUTING; CALIBRATION; GAUSSIAN PROCESSES; REACTORS; SIMULATION; SUBCOOLED BOILING; SYSTEMS ANALYSIS; THERMAL HYDRAULICS; VALIDATION
Citation Formats
Bui, A. V., Dinh, N. T., Nourgaliev, R. R., and Williams, B. J. Statistical modeling support for calibration of a multiphysics model of subcooled boiling flows. United States: N. p., 2013.
Web.
Bui, A. V., Dinh, N. T., Nourgaliev, R. R., & Williams, B. J. Statistical modeling support for calibration of a multiphysics model of subcooled boiling flows. United States.
Bui, A. V., Dinh, N. T., Nourgaliev, R. R., and Williams, B. J. 2013.
"Statistical modeling support for calibration of a multiphysics model of subcooled boiling flows". United States.
@article{osti_22212871,
title = {Statistical modeling support for calibration of a multiphysics model of subcooled boiling flows},
author = {Bui, A. V. and Dinh, N. T. and Nourgaliev, R. R. and Williams, B. J.},
abstractNote = {Nuclear reactor system analyses rely on multiple complex models which describe the physics of reactor neutronics, thermal hydraulics, structural mechanics, coolant physico-chemistry, etc. Such coupled multiphysics models require extensive calibration and validation before they can be used in practical system safety study and/or design/technology optimization. This paper presents an application of statistical modeling and Bayesian inference in calibrating an example multiphysics model of subcooled boiling flows which is widely used in reactor thermal hydraulic analysis. The presence of complex coupling of physics in such a model together with the large number of model inputs, parameters and multidimensional outputs poses significant challenge to the model calibration method. However, the method proposed in this work is shown to be able to overcome these difficulties while allowing data (observation) uncertainty and model inadequacy to be taken into consideration. (authors)},
doi = {},
url = {https://www.osti.gov/biblio/22212871},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jul 01 00:00:00 EDT 2013},
month = {Mon Jul 01 00:00:00 EDT 2013}
}