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An Interpretable Dynamic Feature Search Methodology for Accelerating Computational Process of Control Rod Descent in Nuclear Reactors.

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Title: An Interpretable Dynamic Feature Search Methodology for Accelerating Computational Process of Control Rod Descent in Nuclear Reactors.
Authors: Huang, Qingyu1 (AUTHOR), Xiao, Cong1 (AUTHOR), Zeng, Wei1 (AUTHOR) zengwei02@sina.com, Xu, Le1 (AUTHOR), Liu, Jia1 (AUTHOR), Pang, Zhixin1 (AUTHOR), Lin, Yuanfeng1 (AUTHOR), Zhao, Mengwei1 (AUTHOR), Liu, Xiaobo1 (AUTHOR)
Source: Energies (19961073). Apr2025, Vol. 18 Issue 7, p1827. 17p.
Subjects: Control elements (Nuclear reactors), Machine learning, Similarity (Physics), Computational fluid dynamics, Fluid dynamics, Nuclear reactors
Abstract: Within the operational dynamics of a nuclear reactor, the customary approach involves modulating the reactor's power output by means of control rod manipulation, which effectively alters the neutron density across the core. The descent behavior of the control rod drive lines pertains to the intricate motion exhibited by the control rod components within the reactor during its operational lifespan, characterized by conditions of heightened irradiation, temperature, pressure, and complex fluid dynamics. The precise calculation of the control rod descent process is an integral facet of reactor structural design to ensure the safe and reliable operation of the reactor. However, the current computational fluid dynamics-based simulation methods employed for this purpose necessitate extensive grid computations, imposing significant computational burdens in terms of resources and time. In light of this challenge, we present a novel and interpretative algorithm rooted in dynamic similarity feature search. Through comprehensive validation, this algorithm demonstrates remarkable precision, with the computational results exhibiting an error margin within 10% while simultaneously achieving a substantial enhancement of computational efficiency of nearly three orders of magnitude when compared to conventional computational fluid dynamics techniques and sequence-to-sequence machine learning algorithms. Notably, this algorithm showcases exceptional versatility, holding immense promise for broad applicability across various operational scenarios encountered during the intricate process of nuclear reactor design. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI 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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  Data: An Interpretable Dynamic Feature Search Methodology for Accelerating Computational Process of Control Rod Descent in Nuclear Reactors.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Apr2025, Vol. 18 Issue 7, p1827. 17p.
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  Data: Within the operational dynamics of a nuclear reactor, the customary approach involves modulating the reactor's power output by means of control rod manipulation, which effectively alters the neutron density across the core. The descent behavior of the control rod drive lines pertains to the intricate motion exhibited by the control rod components within the reactor during its operational lifespan, characterized by conditions of heightened irradiation, temperature, pressure, and complex fluid dynamics. The precise calculation of the control rod descent process is an integral facet of reactor structural design to ensure the safe and reliable operation of the reactor. However, the current computational fluid dynamics-based simulation methods employed for this purpose necessitate extensive grid computations, imposing significant computational burdens in terms of resources and time. In light of this challenge, we present a novel and interpretative algorithm rooted in dynamic similarity feature search. Through comprehensive validation, this algorithm demonstrates remarkable precision, with the computational results exhibiting an error margin within 10% while simultaneously achieving a substantial enhancement of computational efficiency of nearly three orders of magnitude when compared to conventional computational fluid dynamics techniques and sequence-to-sequence machine learning algorithms. Notably, this algorithm showcases exceptional versatility, holding immense promise for broad applicability across various operational scenarios encountered during the intricate process of nuclear reactor design. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energies (19961073) is the property of MDPI 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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        Value: 10.3390/en18071827
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        Text: English
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      – SubjectFull: Control elements (Nuclear reactors)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Similarity (Physics)
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      – SubjectFull: Computational fluid dynamics
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      – SubjectFull: Fluid dynamics
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      – SubjectFull: Nuclear reactors
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      – TitleFull: An Interpretable Dynamic Feature Search Methodology for Accelerating Computational Process of Control Rod Descent in Nuclear Reactors.
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            – D: 01
              M: 04
              Text: Apr2025
              Type: published
              Y: 2025
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