Machine Learning Method for Validation of the Computational Model of Nonstationary Xenon Processes in the VVER Reactor Based on the Algorithm of Separation of Variables.
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| Title: | Machine Learning Method for Validation of the Computational Model of Nonstationary Xenon Processes in the VVER Reactor Based on the Algorithm of Separation of Variables. |
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| Authors: | Nikolaev, A. L.1 (AUTHOR) alnikolaev1@bk.ru, Uvakin, M. A.1 (AUTHOR) Uvakin_ma@grpress.podolsk.ru, Antipov, M. V.1 (AUTHOR), Makhin, I. V.1 (AUTHOR), Ryabov, G. A.1 (AUTHOR) |
| Source: | Physics of Atomic Nuclei. Dec2024, Vol. 87 Issue 8, p1030-1038. 9p. |
| Subjects: | Machine learning, Control elements (Nuclear reactors), Artificial intelligence, Separation of variables, Boric acid |
| Abstract: | In the paper, we present a method for validating the KORSAR/GP software package in terms of a mathematical model of nonstationary xenon processes in a VVER reactor that is based on the separation of spatial and temporal variables. The data obtained from various high-power VVER installations in experiments to study the spatial distribution of energy release under nonstationary reactor poisoning conditions caused by the action of various regulators are used. The model is based on the classification of means of affecting reactivity by the type of variable in energy release, which undergoes changes significant for the process as a result of this impact. Nonstationary xenon poisoning processes, which involve control rods of the control and protection systems and water exchange operations with a change in the concentration of boric acid, as well as both of the listed methods, both in the presence of a change in the neutron power of the reactor and when maintaining its constant value, are considered. A machine learning method on the basis of regression analysis making it possible to estimate the error in calculating the parameters of the energy release field under conditions of spatial, temporal, and spatiotemporal feedback of the xenon concentration and regulators is developed. On the basis of the processed experimental data, a training array, which is used for machine learning of this model, is formed. As a result of the developed algorithm, an error estimate for the model of the computing code with allowance for the partial impact of various means of changing the reactivity in a given calculation is made. [ABSTRACT FROM AUTHOR] |
| Copyright of Physics of Atomic Nuclei is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine Learning Method for Validation of the Computational Model of Nonstationary Xenon Processes in the VVER Reactor Based on the Algorithm of Separation of Variables. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nikolaev%2C+A%2E+L%2E%22">Nikolaev, A. L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alnikolaev1@bk.ru</i><br /><searchLink fieldCode="AR" term="%22Uvakin%2C+M%2E+A%2E%22">Uvakin, M. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Uvakin_ma@grpress.podolsk.ru</i><br /><searchLink fieldCode="AR" term="%22Antipov%2C+M%2E+V%2E%22">Antipov, M. V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Makhin%2C+I%2E+V%2E%22">Makhin, I. V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ryabov%2C+G%2E+A%2E%22">Ryabov, G. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Physics+of+Atomic+Nuclei%22">Physics of Atomic Nuclei</searchLink>. Dec2024, Vol. 87 Issue 8, p1030-1038. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Control+elements+%28Nuclear+reactors%29%22">Control elements (Nuclear reactors)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Separation+of+variables%22">Separation of variables</searchLink><br /><searchLink fieldCode="DE" term="%22Boric+acid%22">Boric acid</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the paper, we present a method for validating the KORSAR/GP software package in terms of a mathematical model of nonstationary xenon processes in a VVER reactor that is based on the separation of spatial and temporal variables. The data obtained from various high-power VVER installations in experiments to study the spatial distribution of energy release under nonstationary reactor poisoning conditions caused by the action of various regulators are used. The model is based on the classification of means of affecting reactivity by the type of variable in energy release, which undergoes changes significant for the process as a result of this impact. Nonstationary xenon poisoning processes, which involve control rods of the control and protection systems and water exchange operations with a change in the concentration of boric acid, as well as both of the listed methods, both in the presence of a change in the neutron power of the reactor and when maintaining its constant value, are considered. A machine learning method on the basis of regression analysis making it possible to estimate the error in calculating the parameters of the energy release field under conditions of spatial, temporal, and spatiotemporal feedback of the xenon concentration and regulators is developed. On the basis of the processed experimental data, a training array, which is used for machine learning of this model, is formed. As a result of the developed algorithm, an error estimate for the model of the computing code with allowance for the partial impact of various means of changing the reactivity in a given calculation is made. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Physics of Atomic Nuclei is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1134/S1063778824080210 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1030 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Control elements (Nuclear reactors) Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Separation of variables Type: general – SubjectFull: Boric acid Type: general Titles: – TitleFull: Machine Learning Method for Validation of the Computational Model of Nonstationary Xenon Processes in the VVER Reactor Based on the Algorithm of Separation of Variables. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nikolaev, A. L. – PersonEntity: Name: NameFull: Uvakin, M. A. – PersonEntity: Name: NameFull: Antipov, M. V. – PersonEntity: Name: NameFull: Makhin, I. V. – PersonEntity: Name: NameFull: Ryabov, G. A. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10637788 Numbering: – Type: volume Value: 87 – Type: issue Value: 8 Titles: – TitleFull: Physics of Atomic Nuclei Type: main |
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