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Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning.

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Title: Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning.
Authors: Williams, Quinton J.1 (AUTHOR) qscorp97@gmail.com, Stewart, Ryan H.2 (AUTHOR), Ryan, Emerald2 (AUTHOR), Palmer, Camille J.1 (AUTHOR), Shields, Ashley2 (AUTHOR)
Source: Nuclear Science & Engineering. Sep2026, Vol. 200 Issue 9, p2116-2131. 16p.
Subject Terms: *Microreactors, *Machine learning, *Supervised learning, *Nuclear energy safety measures, *Classification algorithms
Abstract: Microreactors (MRs) pose new challenges for international safeguards. Their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 195343928
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning.
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  Data: <searchLink fieldCode="JN" term="%22Nuclear+Science+%26+Engineering%22">Nuclear Science & Engineering</searchLink>. Sep2026, Vol. 200 Issue 9, p2116-2131. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Microreactors%22">Microreactors</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Nuclear+energy+safety+measures%22">Nuclear energy safety measures</searchLink><br />*<searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Microreactors (MRs) pose new challenges for international safeguards. Their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=195343928
RecordInfo BibRecord:
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        Value: 10.1080/00295639.2025.2560205
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 2116
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        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Supervised learning
        Type: general
      – SubjectFull: Nuclear energy safety measures
        Type: general
      – SubjectFull: Classification algorithms
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      – TitleFull: Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning.
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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