Preliminary Assessment of Criticality Safety Constraints for Swiss Spent Nuclear Fuel Loading in Disposal Canisters

Vasiliev, A.; Herrero, J. J.; Pecchia, M.; Rochman, D.; Ferroukhi, H.; Caruso, S.

doi:10.3390/ma12030494

Cited by 14 publications

(18 citation statements)

References 22 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It must however be noted that the USL used in real licensing applications will usually be refined in order to take into account burnup (see, e.g., Ref. [6]) as well as uncertainties and/or biases due to the employed methods, data as well as SFMS/UNF structures, geometries and materials.…”

Section: Context Of Optimization Assumptions and Strategymentioning

confidence: 99%

“…To achieve this, an optimization of the canister loadings will therefore be needed, and the scope of this paper is to present a methodology that could broaden the options for such optimization. More specifically, for a given initial population of UNF assemblies, the present optimization will aim at finding loading combinations that allow using the exact number of canisters required to dispose of all the available UNF population, while ensuring strict compliance with the two selected safety and design related constraints, namely the neutron multiplication (''k eff '') and the thermal heat output (or ''decay heat'') [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Solans

Rochman

Brazell

et al. 2021

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor k$$_{\mathrm{eff}}$$ eff . In the present approach, a neural network is trained as a surrogate model to evaluate the k$$_{\mathrm{eff}}$$ eff value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister k$$_{\mathrm{eff}}$$ eff and DH values. The k$$_{\mathrm{eff}}$$ eff computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and k$$_{\mathrm{eff}}$$ eff quantities and (2) to minimize DH and k$$_{\mathrm{eff}}$$ eff differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases.

show abstract

Section: Context Of Optimization Assumptions and Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Solans

Rochman

Brazell

et al. 2021

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…This limitation is adequate given the lack of knowledge on the underlying nuclear data distributions. Details on the NUSS methodology, its validation and application studies can be found in [9], [10], [11], [12], [13].…”

Section: Nuss: Nuclear Data Stochastic Sampling With Ace Filesmentioning

confidence: 99%

Assessment of representativity of the PETALE experiments for validation of Swiss LWRs ex-core dosimetry calculations

et al. 2020

Self Cite

View full text Add to dashboard Cite

The international experimental program PETALE will be carried out at the CROCUS research reactor of EPFL. The program aims at measuring neutron penetration in slabs made of materials composing typical LWR reactor pressure vessel. The measurements will be used for code and nuclear data validation and for the evaluation of the reflecting properties of these materials. In this paper the representativity of the PETALE experiments is assessed with respect to operational LWR reactors dosimetry and activation evaluations using the Paul Scherrer Institute (PSI) in-house tool NUSS. The NUSS tool allows the stochastic sampling of nuclear data using covariance matrices available in modern nuclear data libraries and the subsequent running of a Monte Carlo code with the modified data files. The representativity can then be assessed based on the Pearson correlation coefficients. The ultimate goal of the work is first of all to assess if the planned PETALE measurements could be applicable beyond their primary purpose and serve for extending the PSI validation database for LWR reactor dosimetry evaluations. Secondly, provided that the PETALE measurements are found useful for the task above, the information on the correlations between the PETALE neutron detectors’ responses and the reactor dosimetry quantities of interest, shall be presented and discussed.

show abstract

“…Figure taken from ref. [3]  The criticality safety model of the final disposal canister filled with fresh PWR (UO 2 ) fuel assemblies was implemented with the MCNP Monte Carlo framework [4].…”

Section: Preliminary Analysis For Pwr Fuelmentioning

confidence: 99%

“…Figure taken from ref. [3]  Canister loadings based on a determined loading curve can ensure a subcritical system.…”

Section: Preliminary Analysis For Pwr Fuelmentioning

confidence: 99%

Untitled

View full text Add to dashboard Cite

Preliminary Assessment of Criticality Safety Constraints for Swiss Spent Nuclear Fuel Loading in Disposal Canisters

Cited by 14 publications

References 22 publications

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Assessment of representativity of the PETALE experiments for validation of Swiss LWRs ex-core dosimetry calculations

Untitled

Contact Info

Product

Resources

About