CORE SIM+: A flexible diffusion-based solver for neutron noise simulations

Mylonakis, Antonios; Vinai, Paolo; Demazière, Christophe

doi:10.1016/j.anucene.2021.108149

Cited by 19 publications

(1 citation statement)

References 10 publications

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“…All of the aformentioned methodologies were applied to nuclear reactors in Cartesian geometry for which the transfer function can be calculated by the CORE SIM solver [ 12 , 13 ]. The comparison with real plant data is in progress for some pre-Konvoi nuclear reactors [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

Pantera

Stulik

Vidal-Ferràndiz

et al. 2021

Sensors

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This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagnostic techniques, based on the analysis of the so-called “neutron-noise” signals: that is, fluctuations of the neutron flux around the mean value observed in a steady-state power level. The proposed methodology is comprised of three steps: initially, certain reactor core perturbations scenarios are simulated in software, creating the respective perturbation datasets, which are specific to a given reactor geometry; then, the said datasets are used to train deep learning models that learn to identify and locate the given perturbations within the nuclear reactor core; lastly, the models are tested on actual plant measurements. The overall methodology is validated on hexagonal, pre-Konvoi, pressurized water, and VVER-1000 type nuclear reactors. The simulated data are generated by the FEMFFUSION code, which is extended in order to deal with the hexagonal geometry in the time and frequency domains. The examined perturbations are absorbers of variable strength, and the trained models are tested on actual plant data acquired by the in-core detectors of the Temelín VVER-1000 Power Plant in the Czech Republic. The whole approach is realized in the framework of Euratom’s CORTEX project.

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Section: Related Workmentioning

confidence: 99%

Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

Pantera

Stulik

Vidal-Ferràndiz

et al. 2021

Sensors

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Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

Gong,

Li,

Xiao

et al. 2024

NUCL SCI TECH

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The aging of operational reactors leads to increased mechanical vibrations in the reactor interior. The vibration of the in-core sensors near their nominal locations is a new problem for neutronic field reconstruction. Current field-reconstruction methods fail to handle spatially moving sensors. In this study, we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge. Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation, holding the magnitude and location information of the sensors. General convolutional neural networks were used to learn maps from observations to the global field. The proposed method reconstructed multi-physics fields (including fast flux, thermal flux, and power rate) using observations from a single field (such as thermal flux). Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications, particularly within an amplitude of 5 cm around the nominal locations, which led to average relative errors below 5% and 10% in the $$L_2$$ L 2 and $$L_{\infty }$$ L ∞ norms, respectively.

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On the simulation of neutron noise using a discrete ordinates method

Vinai

Demazière

2021

Annals of Nuclear Energy

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CORE SIM+: A flexible diffusion-based solver for neutron noise simulations

Cited by 19 publications

References 10 publications

Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

On the simulation of neutron noise using a discrete ordinates method

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