Prediction of Optical Sensor Behaviors in a Nuclear Reactor Core using Temporal Convolutional Networks

Zekun Wu; Mohamed Bayoumy; Cyril Hnatovsky; Stephen Mihailov; David Carpenter; Ming-Jun Li; Joshua Daw; Kevin P. Chen

doi:10.1364/OFS.2020.Th2.4

Optical Fiber Sensors Conference 2020 Special Edition
OSA Technical Digest (Optica Publishing Group, 2020),
paper Th2.4
•https://doi.org/10.1364/OFS.2020.Th2.4

Prediction of Optical Sensor Behaviors in a Nuclear Reactor Core using Temporal Convolutional Networks

Zekun Wu, Mohamed Bayoumy, Cyril Hnatovsky, Stephen Mihailov, David Carpenter, Ming-Jun Li, Joshua Daw, and Kevin P. Chen

Optical Fiber Sensors 2020

Washington, DC United States
8–12 June 2020
ISBN: 978-1-55752-307-5

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Environmental, Security, Defense, and Industrial Applications; Technology Commercialization and Standardization (Th2)

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Z. Wu, M. Bayoumy, C. Hnatovsky, S. Mihailov, D. Carpenter, M. Li, J. Daw, and K. P. Chen, "Prediction of Optical Sensor Behaviors in a Nuclear Reactor Core using Temporal Convolutional Networks," in Optical Fiber Sensors Conference 2020 Special Edition, G. Cranch, A. Wang, M. Digonnet, and P. Dragic, eds., OSA Technical Digest (Optica Publishing Group, 2020), paper Th2.4.

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Abstract

Fiber optic sensor inscribed in radiation-harden fibers can withstand high neutron flux in an operating nuclear reactor. Machine-learning algorithm using temporal convolutional networks was used to mitigate sensor drift and abnormal event prediction.

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