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Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm
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- Solans, Virginie (author)
- Uppsala universitet,Tillämpad kärnfysik,Ecole Polytech Fed Lausanne EPFL, Sect Phys, Lausanne, Switzerland; Paul Scherrer Inst, Forsch Str 111, CH-5232 Villigen, Switzerland
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- Rochman, Dimitri (author)
- Paul Scherrer Inst, Forsch Str 111, CH-5232 Villigen, Switzerland.
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- Brazell, Christian (author)
- Texas A&M Univ, College Stn, TX 77843 USA.
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- (creator_code:org_t)
- 2021-07-04
- 2021
- English.
- In: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 33:23, s. 16627-16639
- Journal article (peer-reviewed)
Abstract
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- 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 keff. In the present approach, a neural network is trained as a surrogate model to evaluate the keff 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 keff and DH values. The keff 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 keff quantities and (2) to minimize DH and keff 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Keyword
- High-level nuclear waste
- Neural network
- Genetic algorithm
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- ref (subject category)
- art (subject category)
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- ENGINEERING AND TECHNOLOGY
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