Machine learning application to single channel design of molten salt reactor

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

Research output: Contribution to journalArticlepeer-review


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. 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.

Original languageEnglish (US)
Article number108409
JournalAnnals of Nuclear Energy
StatePublished - Oct 2021


  • Channel design
  • Machine learning
  • Molten salt reactor
  • Monte Carlo
  • Optimization
  • Simulation

ASJC Scopus subject areas

  • Nuclear Energy and Engineering


Dive into the research topics of 'Machine learning application to single channel design of molten salt reactor'. Together they form a unique fingerprint.

Cite this