Optimal placement of fixed in-core detectors for Tehran Research Reactor using information theory

Terman, Mohammad Sadegh; Kojouri, N. Mataji; Khalafi, H.

doi:10.1016/j.pnucene.2018.03.012

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…The online FMS uses either a mathematical model of the reactor or a set of precomputed flux shapes also called as modes, to determine the core neutron flux distribution from in-core detector readings, taken as input. The positioning of in-core detectors plays a major role and many earlier studies involved the use of evolutionary optimization algorithms such as Genetic Algorithm, Simulated Annealing, etc., to decide on the locations (Li et al, 2019;Peng et al, 2014;Terman et al, 2018). All these heuristic algorithms were dependent on the proper definition of the objective function.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of AHWR flux mapping system during normal operational scenarios

Anupreethi

Yellapu

Gupta

et al. 2022

Nuclear Engineering and Design

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Section: Introductionmentioning

confidence: 99%

Performance evaluation of AHWR flux mapping system during normal operational scenarios

Anupreethi

Yellapu

Gupta

et al. 2022

Nuclear Engineering and Design

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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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Comparison of three methodologies for optimization of the layout of in-core detectors with a specific number of detectors

Ding

Duan

et al. 2019

Annals of Nuclear Energy

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Optimal placement of fixed in-core detectors for Tehran Research Reactor using information theory

Cited by 7 publications

References 28 publications

Performance evaluation of AHWR flux mapping system during normal operational scenarios

Performance evaluation of AHWR flux mapping system during normal operational scenarios

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

Comparison of three methodologies for optimization of the layout of in-core detectors with a specific number of detectors

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