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Uncertainty Quantification on Spent Nuclear Fuel with LMC | | Arnau Albà
; Andreas Adelmann
; Dimitri Rochman
; | | Date: |
1 Sep 2023 | | Abstract: | The recently developed method Lasso Monte Carlo (LMC) for uncertainty
quantification is applied to the characterisation of spent nuclear fuel. The
propagation of nuclear data uncertainties to the output of calculations is an
often required procedure in nuclear computations. Commonly used methods such as
Monte Carlo, linear error propagation, or surrogate modelling suffer from being
computationally intensive, biased, or ill-suited for high-dimensional settings
such as in the case of nuclear data. The LMC method combines multilevel Monte
Carlo and machine learning to compute unbiased estimates of the uncertainty, at
a lower computational cost than Monte Carlo, even in high-dimensional cases.
Here LMC is applied to the calculations of decay heat, nuclide concentrations,
and criticality of spent nuclear fuel placed in disposal canisters. The
uncertainty quantification in this case is crucial to reduce the risks and
costs of disposal of spent nuclear fuel. The results show that LMC is unbiased
and has a higher accuracy than simple Monte Carlo. | | Source: | arXiv, 2309.00364 | | Services: | Forum | Review | PDF | Favorites | |
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