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

Alsayyari, F.S.; Tiberga, M.; Perko, Z.; Lathouwers, D.; Kloosterman, J.L.

doi:10.1016/j.anucene.2020.107321

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

Title

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

Author

Alsayyari, F.S. (TU Delft RST/Reactor Physics and Nuclear Materials)
Tiberga, M. (TU Delft RST/Reactor Physics and Nuclear Materials)
Perko, Z. (TU Delft RST/Reactor Physics and Nuclear Materials)
Lathouwers, D. (TU Delft RST/Reactor Physics and Nuclear Materials)
Kloosterman, J.L. (TU Delft RST/Radiation, Science and Technology)

Department

RST/Radiation, Science and Technology

Date

2020

Abstract

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 k_eff 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 k_eff and the maximum temperature of the system.

Subject

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