Machine learning-assisted surrogate construction for full-core fuel performance analysis. (arXiv:2104.09499v1 [cs.CE])

Accurately predicting the behavior of a nuclear reactor requires multiphysics
simulation of coupled neutronics, thermal-hydraulics and fuel thermo-mechanics.
The fuel thermo-mechanical response provides essential information for
operational limits and safety analysis. Traditionally, fuel performance
analysis is performed standalone, using calculated spatial-temporal power
distribution and thermal boundary conditions from the coupled
neutronics-thermal-hydraulics simulation as input. Such one-way coupling is
result of the high cost induced by the full-core fuel performance analysis,
which provides more realistic and accurate prediction of the core-wide response
than the “peak rod” analysis. It is therefore desirable to improve the
computational efficiency of full-core fuel performance modeling by constructing
fast-running surrogate, such that fuel performance modeling can be utilized in
the core reload design optimization. This work presents methodologies for
full-core surrogate construction based on several realistic equilibrium PWR
core designs. As a fast and conventional approach, look-up tables are only
effective for certain fuel performance quantities of interest (QoIs). Several
representative machine-learning algorithms are introduced to capture the
complicated physics for other fuel performance QoIs. Rule-based model is useful
as a feature extraction technique to account for the spatial-temporal
complexity of operating conditions. Constructed surrogates achieve at least ten
thousand time acceleration with satisfying prediction accuracy. Current work
lays foundation for tighter coupling of fuel performance modeling into the core
design optimization framework. It also sets stage for full-core fuel
performance analysis with BISON where the computational cost becomes more



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