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Personal profile

Education

  • PhD, Nuclear Engineering, University of Wisconsin - Madison, 2013
  • BA, Physics, University of Chicago, 2008

Research Interests

  • Scientific computing education
  • Repository system performance modeling
  • Open & reproducible scientific computing
  • Computational methods for optimization and prediction
  • Coupled multi-physics simulation
  • Reactor neutronics and kinetics
  • Nuclear fuel cycle simulation
  • Advanced nuclear reactor modeling and simulation

Teaching

  • NPRE412, Fall 2016

Keywords

  • TK Electrical engineering. Electronics Nuclear engineering
  • QC Physics
  • QA76 Computer software

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Research Output

Deep learning approach to nuclear fuel transmutation in a fuel cycle simulator

Bae, J. W., Rykhlevskii, A., Chee, G. & Huff, K. D., May 2020, In : Annals of Nuclear Energy. 139, 107230.

Research output: Contribution to journalArticle

  • Fuel cycle performance of fast spectrum molten salt reactor designs

    Rykhlevskii, A., Betzler, B. R., Worrall, A. & Huff, K., Jan 1 2019, International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019. American Nuclear Society, p. 342-353 12 p. (International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Modeling and simulation of online reprocessing in the thorium-fueled molten salt breeder reactor

    Rykhlevskii, A., Bae, J. W. & Huff, K. D., Jun 2019, In : Annals of Nuclear Energy. 128, p. 366-379 14 p.

    Research output: Contribution to journalArticle

  • Standardized verification of the CYCLUS fuel cycle simulator

    Bae, J. W., Peterson-Droogh, J. L. & Huff, K. D., Jun 2019, In : Annals of Nuclear Energy. 128, p. 288-291 4 p.

    Research output: Contribution to journalArticle

  • Synergistic spent nuclear fuel dynamics within the European Union

    Bae, J. W., Singer, C. E. & Huff, K. D., Jul 2019, In : Progress in Nuclear Energy. 114, p. 1-12 12 p.

    Research output: Contribution to journalArticle

  • Datasets

    Automated Isotope Identification and Quantification Using Artificial Neural Networks

    Kamuda, M. (Creator), Huff, K. D. (Creator), University of Illinois at Urbana-Champaign, Dec 12 2019

    Dataset

    Honors & Recognition

    File
  • Young Member Excellence Award

    Kathryn D Huff (Recipient), Nov 2016

    Prize: Honorary award