Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification
This work serves as a proof of concept for an automated tool to assist in the evaluation of experimental neutron cross section data in the resolved resonance range. The resonance characterization problem is posed as a mixed integer nonlinear program (MINLP). Since the number of resonances present is...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_16004.pdf |
| _version_ | 1797807660696862720 |
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| author | Walton Noah Armstrong Jordan Medal Hugh Sobes Vladimir |
| author_facet | Walton Noah Armstrong Jordan Medal Hugh Sobes Vladimir |
| author_sort | Walton Noah |
| collection | DOAJ |
| description | This work serves as a proof of concept for an automated tool to assist in the evaluation of experimental neutron cross section data in the resolved resonance range. The resonance characterization problem is posed as a mixed integer nonlinear program (MINLP). Since the number of resonances present is unknown, the model must be able to be determine the number of parameters to properly characterize the cross section curve as well as calculate the appropriate values for those parameters. Due to the size of the problem and the nonconvex nature of the parameterization, the optimization formulation is too difficult to solve as whole. A novel method is developed to decompose the problem into smaller, solvable windows and then stitch them back together via parameter-cardinality and parameter-value agreement routines in order to achieve a global solution. A version of quantile regression is used to provide an uncertainty estimate on the suggested cross section that is appropriate with respect to the experimental data. The results demonstrate the model's ability to find the proper number of resonances, appropriate average values for the parameters, and an uncertainty estimation that is directly reflective of the experimental conditions. The use of synthetic data allows access to the solution, this is leveraged to build-up performance statistics and map the uncertainty driven by the experimental data to an uncertainty on the true cross section. |
| first_indexed | 2024-03-13T06:25:58Z |
| format | Article |
| id | doaj.art-9744d67e7b3541a0a7332c136e9f8b9d |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-03-13T06:25:58Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-9744d67e7b3541a0a7332c136e9f8b9d2023-06-09T09:18:03ZengEDP SciencesEPJ Web of Conferences2100-014X2023-01-012841600410.1051/epjconf/202328416004epjconf_nd2023_16004Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantificationWalton Noah0Armstrong Jordan1Medal Hugh2Sobes Vladimir3The University of Tennessee, Nuclear Engineering DepartmentDepartment of Mathematical Sciences, U.S. Air Force Academy, Air Force AcademyThe University of Tennessee, Department of Industrial & Systems EngineeringThe University of Tennessee, Nuclear Engineering DepartmentThis work serves as a proof of concept for an automated tool to assist in the evaluation of experimental neutron cross section data in the resolved resonance range. The resonance characterization problem is posed as a mixed integer nonlinear program (MINLP). Since the number of resonances present is unknown, the model must be able to be determine the number of parameters to properly characterize the cross section curve as well as calculate the appropriate values for those parameters. Due to the size of the problem and the nonconvex nature of the parameterization, the optimization formulation is too difficult to solve as whole. A novel method is developed to decompose the problem into smaller, solvable windows and then stitch them back together via parameter-cardinality and parameter-value agreement routines in order to achieve a global solution. A version of quantile regression is used to provide an uncertainty estimate on the suggested cross section that is appropriate with respect to the experimental data. The results demonstrate the model's ability to find the proper number of resonances, appropriate average values for the parameters, and an uncertainty estimation that is directly reflective of the experimental conditions. The use of synthetic data allows access to the solution, this is leveraged to build-up performance statistics and map the uncertainty driven by the experimental data to an uncertainty on the true cross section.https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_16004.pdf |
| spellingShingle | Walton Noah Armstrong Jordan Medal Hugh Sobes Vladimir Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification EPJ Web of Conferences |
| title | Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification |
| title_full | Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification |
| title_fullStr | Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification |
| title_full_unstemmed | Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification |
| title_short | Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification |
| title_sort | automated resonance evaluation non convex decomposition method for resonance regression and uncertainty quantification |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_16004.pdf |
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