Validation of Serpent-SUBCHANFLOW-TRANSURANUS pin-by-pin burnup calculations using experimental data from the Temelín II VVER-1000 reactor

García, Manuel; Vočka, Radim; Tuominen, Riku; Gommlich, André; Leppänen, Jaakko; Valtavirta, Ville; Imke, Uwe; Ferraro, Diego; Uffelen, P. Van; Milisdorfer, L.; Sánchez-Espinoza, Víctor Hugo

doi:10.1016/j.net.2021.04.023

Cited by 20 publications

(3 citation statements)

References 12 publications

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“…To conduct pin-by-pin burnup computations, Garcia et al propose a simulation tool based on Serpent 2, SUBCHANFLOW (SCF), and TRANSURANUS (TU) [13]. The technique consolidates fuel-performance analysis, Monte Carlo neutron transport, and subchannel thermal hydraulics to simulate depletion in a highly detailed manner.…”

Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

“…The method can be used to quantify burnup-dependent safety variables at the pin levels for various designs of NPPs [12]. Markedly, an object-oriented setup featuring a mesh-feedback exchange is the basis for the Serpent-SCF-TU coupling system [13]. Serpent 2's novel Collision-based Domain Decomposition (CDD) scheme spreads burnable materials across computation nodes, facilitating large-scale burnup calculations.…”

Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

“…Serpent 2's novel Collision-based Domain Decomposition (CDD) scheme spreads burnable materials across computation nodes, facilitating large-scale burnup calculations. In particular, this technique defines fields with only the related data and a fraction of the combustible materials, while replicating the rest of the model data in other domains [13]. Thanks to this system, it is possible to derive solutions to problems that cannot be accommodated by one node.…”

Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

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Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Almteiri¹,

Kim²

2022

Energies

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The United Arab Emirates (UAE) built four nuclear power plants at the Barakah site to supply 25% of the region’s electricity. Among the Barakah Nuclear Power Plants, (BNPPs), their main objectives are to achieve the highest possible safety for the environment, operators, and community members; quality nuclear reactors and energy; and power production efficiency. To meet these objectives, decision-makers must access large amounts of data in the case of a nuclear accident to prevent the release of radioactive materials. Machine learning offers a feasible solution to propose early warnings and help contain accidents. Thus, our study aimed at developing and testing a machine learning model to classify nuclear accidents using the associated release of radioactive materials. We used Radiological Assessment System for Consequence Analysis (RASCAL) software to estimate the concentration of released radioactive materials in the four seasons of the year 2020. We applied these concentrations as predictors in a classification tree model to classify three types of severe accidents at Unit 1 of BNPPs each season. The average accuracy of the classification models in the four seasons was 97.3% for the training data and 96.5% for the test data, indicating a high efficacy. Thus, the generated classification models can distinguish between the three simulated accidents in any season.

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Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

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