A neural network approach for burn-up calculation and its application to the dynamic fuel cycle code CLASS

Leniau, B.; Mouginot, B.; Thiollière, Nicolas; Doligez, X.; Bidaud, A.; Courtin, Fanny; Ernoult, M.; David, S.

doi:10.1016/j.anucene.2015.03.035

Cited by 27 publications

(18 citation statements)

References 2 publications

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“…Multi-Layer Perceptron from the library TMVA [25] is used with success for this purpose. It has been shown [15] that neural network neutron data prediction precision is close to Monte-Carlo uncertainty in the standard range of application.…”

Section: Reactor Modelsmentioning

confidence: 99%

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A methodology for performing sensitivity analysis in dynamic fuel cycle simulation studies applied to a PWR fleet simulated with the CLASS tool

Thiollière

Clavel

Courtin

et al. 2018

EPJ Nuclear Sci. Technol.

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Fuel cycle simulators are used worldwide to provide scientific assessment to fuel cycle future strategies. Those tools help understanding the fuel cycle physics and determining the most impacting drivers at the cycle scale. A standard scenario calculation is usually based on a set of operational assumptions, such as reactor Burn-Up, deployment history, cooling time, etc. Scenario output is then the evolution of isotopes mass in the facilities that composes the nuclear fleet. The increase of computing capacities and the use of neutron data fast predictors provide new opportunities in nuclear scenario studies. Indeed, a very high number of calculations is possible, which allows testing a high number of operational assumptions combinations. The global sensitivity analysis (GSA) formalism is specifically well adapted for this kind of problem. In this new framework, a scenario study is based on the sampling of operational data, which become input variables. A first result of a scenario study is the highlight of relations between operational input data and outputs. Input variable subspace that satisfy optimization criteria on an output, such as plutonium incineration or stabilization, can also be determined. In this paper, a focus is made on the methodology based on GSA. This innovative methodology is presented and applied to a simple fleet simulation composed of a PWR-UOx fuel and a PWR-MOx fuel. Calculations are done with the fuel cycle simulator CLASS developed by the CNRS/IN2P3 in collaboration with IRSN. The design of experiment is built from five fuel cycle input sampled variables. Sensitivity indices have been calculated on plutonium and minor actinide (MA) production. It shows that the PWR-UOx Burn-Up and the fraction of PWR-MOx fuel are the most important input variables that explain the plutonium production. For the MA production, main drivers depend strongly on isotopes. Sensitivity analysis also reveals input variable subspace responsible of simulation crash, what led to an important improvement of the model algorithms. An equilibrium condition on the plutonium mass in the stockpile used for building MOx fuel has been applied. The solution is represented as a subspace in the PWR-UOx Burn-Up and PWR-MOx fraction input space. For instance, achieving a plutonium equilibrium in a stockpile fed by a PWR-UOx that operates at 40 GWd/t requires a PWR-MOx fraction between 9 and 14%. This study also provides data related to plutonium incineration induced by the utilization of the MOx.

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Section: Reactor Modelsmentioning

confidence: 99%

“…for any available materials. This could be achieved for instance by a simple formula, by the use of 239 Pu equivalent methods [14] or with neural network based predictors [15]. An additional algorithm is then needed to determine the appropriate composition built from available stocks.…”

Section: Dynamic Fuel Cycle Studiesmentioning

confidence: 99%

A methodology for performing sensitivity analysis in dynamic fuel cycle simulation studies applied to a PWR fleet simulated with the CLASS tool

Thiollière

Clavel

Courtin

et al. 2018

EPJ Nuclear Sci. Technol.

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“…The dynamic fuel cycle simulation is performed by the simulator CLASS, which employs artificial neuron networks to build simulation models [11]. For example, the equivalence model predicts the fissile content of fresh fuel for a given burn-up according to the isotopic composition of resources; the irradiation model simulates the evolution of material in the reactor.…”

Section: Simulation Of the French Fleet By Classmentioning

confidence: 99%

“…The MOX fuel follows the same steps in the cycle, except that the MOX SFs are not reprocessed because the Pu is mono-recycled in this example. Descriptions in detail of physics model building and facility simulation by CLASS can be found in [11].…”

Section: Simulation Of the French Fleet By Classmentioning

confidence: 99%

Assessment of strategy robustness under disruption of objective in dynamic fuel cycle studies

Liang

Ernoult

Doligez

et al. 2021

Annals of Nuclear Energy

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The future of nuclear power depends on the interests and decisions. To take the relevant deep uncertainty into account, a new methodology of robustness analysis of fuel cycle strategies has been developed. The method has been applied to the French cycle, considering the future deployment of fast reactor as the preselected objective and an uncertain change, called disruption, towards a new objective: the minimization of transuranic inventories without fast reactor. The status of plutonium is contradictory in two cases. Two approaches of identifying robust strategies were tested, which correspond respectively to the static and adaptive robustness assessment. One identifies static strategies in a pre-disruption scenario, which achieve acceptable outcomes for both objectives. The other takes a trajectory pursuing the pre-selected objective and, in case of disruption, adapts it for the new objective. The comparison of two approaches indicates the temporality of adaptation relative to immediate actions under the uncertain disruption.

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“…In this work, the trajectories of nuclear fuel cycle are simulated by CLASS [10]. Only models of PWR UOX and PWR MOX [11] are applied, while SFR is not simulated.…”

Section: Predisruption Scenariomentioning

confidence: 99%

Robustness Study of Electro-Nuclear Scenario under Disruption

Liang

Ernoult

Doligez

et al. 2021

JNE

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As the future of nuclear power is uncertain, only choosing one development objective for the coming decades can be risky; while trying to achieve several possible objectives at the same time may lead to a deadlock due to contradiction among them. In this work, we study a simple scenario to illustrate the newly developed method of robustness study, which considers possible change of objectives. Starting from the current French fleet, two objectives are considered regarding the possible political choices for the future of nuclear power: A. Complete substitution of Pressurized Water Reactors by Sodium-cooled Fast Reactors in 2180; B. Minimization of all potential nuclear wastes without SFR deployment in 2180. To study the robustness of strategies, the disruption of objective is considered: the objective to be pursued is possibly changed abruptly from A into B at unknown time. To minimize the consequence of such uncertainty, the first option is to identify a robust static strategy, which shows the best performance for both objectives A and B in the predisruption situation. The second option is to adapt a trajectory which pursues initially objective A, for objective B in case of the disruption. To identify and to analyze the adaptively robust strategies, outcomes of possible adaptations upon a given trajectory are compared with the robust static optimum. The temporality of adaptive robustness is analyzed by investigating different adaptation times.

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A neural network approach for burn-up calculation and its application to the dynamic fuel cycle code CLASS

Cited by 27 publications

References 2 publications

A methodology for performing sensitivity analysis in dynamic fuel cycle simulation studies applied to a PWR fleet simulated with the CLASS tool

A methodology for performing sensitivity analysis in dynamic fuel cycle simulation studies applied to a PWR fleet simulated with the CLASS tool

Assessment of strategy robustness under disruption of objective in dynamic fuel cycle studies

Robustness Study of Electro-Nuclear Scenario under Disruption

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