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A deep-learning representation of multi-group cross sections in lattice calculations
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Engineering.
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Engineering.ORCID iD: 0000-0002-7943-7517
2024 (English)In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 195, article id 110123Article in journal (Refereed) Published
Abstract [en]

To compute few-group nodal cross sections, lattice codes must first generate multi-group cross sections using continuous energy cross-section libraries for each material in each fuel cell. Since the processing cost is significant, we propose representing the multi-group cross sections during lattice calculations using a pre-trained deep-learning-based model. The model utilizes a combination of Principal Component Analysis (PCA) and fully connected Neural Networks (NN). The model is specifically designed to manage extensive multi-group cross-section data sets, which contain data for several dozen nuclides and encompass more than 50 energy groups. Our testing of the trained model on a PWR assembly with a realistic boron letdown curve revealed an average relative error of around 0.1% for both fission and total macroscopic cross sections. The average time required for the model to generate the cross sections was approximately 0.01% of the time needed to execute the cross-section processing module.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 195, article id 110123
Keywords [en]
Cross section representation, Deep learning, Lattice codes, Neural network, Principal component analysis
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-338077DOI: 10.1016/j.anucene.2023.110123ISI: 001086078700001Scopus ID: 2-s2.0-85172674338OAI: oai:DiVA.org:kth-338077DiVA, id: diva2:1804673
Note

QC 20231013

Available from: 2023-10-13 Created: 2023-10-13 Last updated: 2023-11-10Bibliographically approved

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Chan, Yi MengDufek, Jan

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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