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

Abstract

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.

Data-driven machine learning for disposal of high-level nuclear waste: A review

“…Table 1 Main research on the status of the DDML for the disposal of HLW. Unsupervised learning type (Lu et al, 2021); (Sirdesai et al, 2019); (Yoon et al, 2019) Artificial neural network (ANN) Supervised learning type (Solans et al, 2021); (Elodie et al, 2020); (Tsai et al, 2019) Genetic algorithm (GA) Supervised or Unsupervised learning type (Suh et al, 2020); (Xu et al, 2020) Clustering method (CM) Unsupervised learning type (Stanfill et al, 2020); (Suh et al, 2018) Logistic regression Supervised learning type Deep…”

“…A GA algorithm is then developed to optimize simultaneously the effective neutron multiplication factor k eff and decay heat of the SNF canister as shown in Fig. 7 (Solans et al, 2021). In Fig.…”

“…Finally, the DDML coupled with physical constraints, theoretical equations and relations can enhance the DDML performance (Solans et al, 2021;Tian et al, 2021;Hu et al, 2021a;Krishnan et al, 2018). Standard DDML algorithms lack important physics and mechanisms.…”

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

“…Therefore, the hybrid prediction model combining WNN and GA is presented to search a globally near-optimal combination of network pa-rameters, which can improves the accuracy of WNN. [22,29,34,35] have attained preferable results in training neural networks with GA, but there are few studies on WNN with adaptive GA in shipping market.…”

Spent Nuclear Fuel Passive Gamma Analysis And Reproducibility: Application to Skb-50 Assemblies

“…The application by SKB includes extensive safety assessments aiming to ensure the safe storage of the SNFs over long periods of time, and these assessments include limits on, for instance, criticality and decay heat in the final storage. Decay heat especially is a safety criteria because the temperature at the host rock needs to be limited in order to avoid irreversible structural changes to the host rock and the integrity of the clay buffer around the copper canister (Solans et al, 2021a). These safety parameters will be estimated with detailed calculations for each specific SNF before encapsulation using state-of-the-art codes.…”