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This is a Quantitative Study study (also classified as: Experimental Study).

October 2, 2025Open Access

Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components

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The convolutional neural network enabled accurate predictions of temperature distribution in a nuclear reactor fuel rod.
The methodology employed coupled simulations with BISON and MOOSE-THM to generate relevant datasets for training.
Results showed high accuracy without overfitting, enhancing the potential for real-time monitoring in nuclear power plants.
Proactive predictive maintenance development could significantly reduce operational downtime in nuclear reactors.

Authors

LMLuiz Aldeia MachadoPennsylvania State University VLVictor Coppo LeiteIdaho National Laboratory EMElia MerzariPennsylvania State University

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Implication

This research demonstrates a CNN approach for real-time thermomechanical monitoring in nuclear reactors, suggesting enhanced predictive maintenance capabilities.

Cite This Study

Machado et al. (2025) studied this question.

synapsesocial.com/papers/68de5da283cbc991d0a207a3 https://doi.org/10.48550/arxiv.2507.09443

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Implication

This research demonstrates a CNN approach for real-time thermomechanical monitoring in nuclear reactors, suggesting enhanced predictive maintenance capabilities.

Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components

Key Points

Discussion

Implication

Cite This Study

Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components

Key Points

Discussion

Implication

Cite This Study