Loading optimization for Swiss used nuclear fuel assemblies into final disposal canisters

Brazell

et al. 2021

Neural Comput & Applic

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.

Section: Context Of Optimization Assumptions and Strategymentioning

confidence: 99%

“…Indeed, not all UNFs will be available at once at the docking/undocking stations of the final repository. A limited amount of transport casks can be unloaded at once, thus requiring a cask selection prior to the canister loading [21,32]. Such practical limitation is not considered in this work.…”

Section: Current Limitations and Future Improvementsmentioning

confidence: 99%

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

Solans

Brazell

et al. 2021

Neural Comput & Applic

“…The knowledge of the SNF has also an economical aspect. For instance, countries having selected long-term solutions such as deep geological repositories need to optimize the cost of such facilities with their safety aspect [3]. In simple terms, the amount of SNF to be stored directly influences the cost of such a solution.…”

Section: Introductionmentioning

confidence: 99%

On the estimation of nuclide inventory and decay heat: a review from the EURAD European project

EPJ Nuclear Sci. Technol.

Dagan²,

Fiorito

et al. 2023

In this work, a study dedicated to the characterization of the neutronics aspect of the Spent Nuclear Fuel (SNF), as part of the European project EURAD (Work Package 8), is presented. Both measured nuclide concentrations from Post Irradiation Examination samples and decay heat from calorimetric measurements are compared to simulations performed by different partners of the project. Based on these detailed studies and data from the published literature, recommendations are proposed with respect to best practices for SNF modelling, as well as biases and uncertainties for a number of important nuclides and the SNF decay heat for a cooling period from 1 to 1000 years. Finally, specific needs are presented for the improvement of current code prediction capabilities.

“…If the required volume can be reduced by 10% due to more accurate design calculations important costs savings for the ~500 M€ (SKB, 2017) worth of tunnel construction would follow. In another study (Solans et al, 2020) the potential for cost savings through optimization of cask loading was analyzed and it was concluded that with current assumptions on decay heat uncertainty the number of canisters can be reduced by about 2%. This represents relevant saving potential because canisters are big cost items which cost more than 10 M€ each.…”

Section: Introductionmentioning

confidence: 99%

Note on the potential to increase the accuracy of source term calculations for spent nuclear fuel

Seidl¹,

Schillebeeckx

2023

Front. Energy Res.

The accuracy of source term predictions is an important factor which determines the efficiency of interim and final storage of spent nuclear fuel. To minimize the required number of storage containers and to minimize the volume and mass of facilities while maintaining safety margins requires accurate codes to predict the decay heat and the gamma and neutron sources with minimum bias for time points ranging from months to thousands of years. While the relevant nuclear data for the purpose of criticality safety received high attention in the last decades and have been extensively verified with dedicated tests, nuclear data relevant for spent nuclear fuel had smaller priority. Mostly results from a radiochemical analysis of samples taken from commercially irradiated fuel have been used for validation purposes. The comparatively sparse data available from tests which exclusively focus on nuclide vector validation under research conditions means that many factors enter the uncertainty estimate of the measurement-theory comparisons and limits the ability to validate codes to a high accuracy. Firstly, the current status of validation efforts is reviewed. Secondly, fields of improvement are explored. Thirdly, the character of uncertainty distributions in measurement-theory comparisons (C/E) of nuclide vectors is analyzed. Currently there are indications that the C/E data is thick tailed which limits improvement of code validation efforts.