Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation
System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of th...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2015/839249 |
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| author | Joseph P. Yurko Jacopo Buongiorno Robert Youngblood |
| author_facet | Joseph P. Yurko Jacopo Buongiorno Robert Youngblood |
| author_sort | Joseph P. Yurko |
| collection | DOAJ |
| description | System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This work uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process. |
| format | Article |
| id | doaj-art-637ee3e8bbea452e86c2e30db9ab94bf |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-637ee3e8bbea452e86c2e30db9ab94bf2025-02-03T06:07:08ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832015-01-01201510.1155/2015/839249839249Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and FormulationJoseph P. Yurko0Jacopo Buongiorno1Robert Youngblood2MIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USAMIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USAINL, P.O. Box 1625, Idaho Falls, ID 83415-3870, USASystem codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This work uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.http://dx.doi.org/10.1155/2015/839249 |
| spellingShingle | Joseph P. Yurko Jacopo Buongiorno Robert Youngblood Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation Science and Technology of Nuclear Installations |
| title | Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation |
| title_full | Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation |
| title_fullStr | Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation |
| title_full_unstemmed | Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation |
| title_short | Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation |
| title_sort | demonstration of emulator based bayesian calibration of safety analysis codes theory and formulation |
| url | http://dx.doi.org/10.1155/2015/839249 |
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