A nonintrusive adaptive reduced order modeling approach for a molten salt reactor system

Fahad Alsayyari*, Marco Tiberga, Zoltán Perkó, Danny Lathouwers, Jan Leen Kloosterman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)
121 Downloads (Pure)


We use a novel nonintrusive adaptive Reduced Order Modeling method to build a reduced model for a molten salt reactor system. Our approach is based on Proper Orthogonal Decomposition combined with locally adaptive sparse grids. Our reduced model captures the effect of 27 model parameters on keff of the system and the spatial distribution of the neutron flux and salt temperature. The reduced model was tested on 1000 random points. The maximum error in multiplication factor was found to be less than 50 pcm and the maximum L2 error in the flux and temperature were less than 1%. Using 472 snapshots, the reduced model was able to simulate any point within the defined range faster than the high-fidelity model by a factor of 5×106. We then employ the reduced model for uncertainty and sensitivity analysis of the selected parameters on keff and the maximum temperature of the system.

Original languageEnglish
Article number107321
Number of pages11
JournalAnnals of Nuclear Energy
Publication statusPublished - 2020


  • Data-driven
  • Greedy
  • Locally adaptive sparse grids
  • Machine learning
  • Molten salt reactor
  • Nonintrusive
  • Proper Orthogonal Decomposition
  • Reduced Order Modelling
  • Sensitivity analysis
  • Uncertainty quantification


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