Machine learning application to single channel design of molten salt reactor

Turkmen, Mehmet; Chee, Gwendolyn J.Y.; Huff, Kathryn D.

doi:10.1016/j.anucene.2021.108409

Machine learning application to single channel design of molten salt reactor

Journal Article · Fri Jun 11 00:00:00 EDT 2021 · Annals of Nuclear Energy

DOI:https://doi.org/10.1016/j.anucene.2021.108409· OSTI ID:1976812

Turkmen, Mehmet ^[1]; Chee, Gwendolyn J.Y. ^[1]; Huff, Kathryn D. ^[1]

University of Illinois at Urbana-Champaign, IL (United States)

This study proposes a robust approach to quickly design a nuclear reactor core and explores the best performing machine learning (ML) technique for predicting feature parameters of the core. Here we implemented the approach into a hypothetical channel of molten salt reactors to demonstrate the applicability of the method. We prepared a Python tool, named Plankton, which couples to a reactor physics code and an optimization tool, and imports ML methods. The tool performs three consecutive phases: reactor database generation, machine learning application, and design optimization. We identified the extra trees method as the best performing estimator. With the estimator, we found nine optimum designs in total, one for each fuel-salt pair, and estimated all the performance metrics of the designs with a <5% prediction error compared to their actual values. U-Pu-NaCl fuel-salt gave promising results with the highest conversion ratio, the most negative feedback coefficient, and the lowest fast flux.

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Research Organization:: Univ. of Illinois at Urbana-Champaign, IL (United States); Univ. of Michigan, Ann Arbor, MI (United States); North Carolina State University, Raleigh, NC (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E); National Science Foundation (NSF); University of Illinois; Scientific and Technological Research Council of Turkey (TUBITAK); International Institute for Carbon Neutral Energy Research; Japanese Ministry of Education, Culture, Sports, Science and Technology; USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation

Grant/Contract Number:: AR0000983; NA0002534; NA0002576; OCI-0725070; ACI-1238993; BIDEB-2219; WPI-I2CNER

OSTI ID:: 1976812

Alternate ID(s):: OSTI ID: 1788099

Journal Information:: Annals of Nuclear Energy, Vol. 161, Issue C; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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