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Communication Dans Un Congrès Année : 2023

Nuclear Data Uncertainties and Adjustments Using Deterministic and Monte-Carlo Methods along with PWR Measurements

Dimitri Rochman
  • Fonction : Collaborateur
  • PersonId : 1253730
Eric Dumonteil
Fausto Malvagi
Alain Hébert
  • Fonction : Directeur scientifique
  • PersonId : 1066282

Résumé

Nuclear power plants safety relies partly on predictions from numerical simulations. Their uncertainties must therefore be evaluated. In PWR reactor physics, the simulations' uncertainties are nowadays estimated on the basis of the differences between existing measurements and the corresponding simulations. This approach leads to weaknesses in the estimation of uncertainties. For example, a novelty can induce more deviations than those seen in the past. Moreover, in some (common) cases, these uncertainties are based on differences between measurements and adjusted simulations, i.e. carried out after the measurements and by forcing the simulations as close as possible to the measurements. The residual deviation thus leads to consider compressed uncertainties. This method of evaluating uncertainties can therefore be improved. To further increase robustness, the approach proposed in a recently published Ph.D. thesis is to return to the fundamentals : the sources of errors in the simulations. The two main types discussed in this paper are the following. On the one hand, physical and numerical approximations introduce deterministic biases, which can be evaluated as the difference with Monte-Carlo. On the other hand, the nuclear data uncertainties, that arise from the limited human understanding of nuclear physics, are propagated in this article with a Total Monte-Carlo approach.
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Dates et versions

irsn-04095487 , version 1 (11-05-2023)
irsn-04095487 , version 2 (11-07-2023)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

  • HAL Id : irsn-04095487 , version 2

Citer

Vivian Salino, Dimitri Rochman, Eric Dumonteil, Fausto Malvagi, Alain Hébert. Nuclear Data Uncertainties and Adjustments Using Deterministic and Monte-Carlo Methods along with PWR Measurements. M&C 2023 - The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, American Nuclear Society, Aug 2023, Niagara Falls, Ontario, Canada. ⟨irsn-04095487v2⟩

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