Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods
Introduction - Advances in computation have brought about significant improvements in creating fast-running high-fidelity simulations of nuclear cores. The BEAVRS benchmark [1] is a highly-detailed PWR specification with two cycles of measured operational data used to validate high-fidelity cor...
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| Format: | Article |
| Language: | en_US |
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American Nuclear Society
2017
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| Online Access: | http://hdl.handle.net/1721.1/109793 https://orcid.org/0000-0002-8876-4878 https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 |
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| author | Kumar, Shikhar Liang, Jingang Forget, Benoit Robert Yves Smith, Kord S. |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Kumar, Shikhar Liang, Jingang Forget, Benoit Robert Yves Smith, Kord S. |
| author_sort | Kumar, Shikhar |
| collection | MIT |
| description | Introduction - Advances in computation have brought about significant improvements in creating fast-running high-fidelity simulations of nuclear cores. The BEAVRS benchmark [1] is a highly-detailed PWR specification with two cycles of measured operational data used to validate high-fidelity core analysis methods. This PWR depletion benchmark captures the fine details of the LWR fuel assemblies, burnable absorbers, in-core fission detectors, core loading and shuffling patterns. Specifically, 58 of the 193 assemblies contain in-core detectors with measurements taken over 61 axial positions every month.
These detectors are U-235 fission chambers with slightly varying mass of U-235. The collected signals are normalized on a given assembly permitting full core comparisons. The fuel layout for cycle 1 and instrument tube locations for the reactor are given in figures 1 and 2 respectively. Through a series of data processing and comparisons, it was shown [2] that axially integrated radial maps of reaction rates were in close
agreement between provided detector data and calculated data |
| first_indexed | 2024-09-23T17:04:19Z |
| format | Article |
| id | mit-1721.1/109793 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:04:19Z |
| publishDate | 2017 |
| publisher | American Nuclear Society |
| record_format | dspace |
| spelling | mit-1721.1/1097932022-10-03T10:12:44Z Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods Kumar, Shikhar Liang, Jingang Forget, Benoit Robert Yves Smith, Kord S. Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Kumar, Shikhar Liang, Jingang Forget, Benoit Robert Yves Smith, Kord S. Introduction - Advances in computation have brought about significant improvements in creating fast-running high-fidelity simulations of nuclear cores. The BEAVRS benchmark [1] is a highly-detailed PWR specification with two cycles of measured operational data used to validate high-fidelity core analysis methods. This PWR depletion benchmark captures the fine details of the LWR fuel assemblies, burnable absorbers, in-core fission detectors, core loading and shuffling patterns. Specifically, 58 of the 193 assemblies contain in-core detectors with measurements taken over 61 axial positions every month. These detectors are U-235 fission chambers with slightly varying mass of U-235. The collected signals are normalized on a given assembly permitting full core comparisons. The fuel layout for cycle 1 and instrument tube locations for the reactor are given in figures 1 and 2 respectively. Through a series of data processing and comparisons, it was shown [2] that axially integrated radial maps of reaction rates were in close agreement between provided detector data and calculated data United States. Department of Energy (Nuclear Energy University Program Grant) 2017-06-12T16:49:26Z 2017-06-12T16:49:26Z 2016-05 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/109793 Kumar, Shikhar, Jingang Liang, Benoit Forget and Kord Smith. "Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods." PHYSOR 2016. Unifying Theory and Experiments in the 21st Century (May 1-5, 2016) https://orcid.org/0000-0002-8876-4878 https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 en_US www.ans.org/meetings/file/682 ANS Winter Meeting & Expo. PHYSOR 2016. Unifying Theory and Experiments in the 21st Century Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Nuclear Society Prof. Forget via Chris Sherratt |
| spellingShingle | Kumar, Shikhar Liang, Jingang Forget, Benoit Robert Yves Smith, Kord S. Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods |
| title | Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods |
| title_full | Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods |
| title_fullStr | Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods |
| title_full_unstemmed | Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods |
| title_short | Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods |
| title_sort | quantifying transient uncertainty in the beavrs benchmark using time series analysis methods |
| url | http://hdl.handle.net/1721.1/109793 https://orcid.org/0000-0002-8876-4878 https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 |
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