AI Enabled Neutron Flux Measurement and Virtual Calibration in Boiling Water Reactors

• URL: http://arxiv.org/abs/2409.17405v1
• Date: Wed, 25 Sep 2024 22:30:09 GMT
• Title: AI Enabled Neutron Flux Measurement and Virtual Calibration in Boiling Water Reactors
• Authors: Anirudh Tunga, Jordan Heim, Michael Mueterthies, Thomas Gruenwald and Jonathan Nistor
• Abstract summary: Accurately capturing the three dimensional power distribution within a reactor core is vital for ensuring the safe and economical operation of the reactor. Machine learning (ML) is being used to solve the problems to reduce maintenance costs, improve the accuracy of online local power measurements, and decrease the bias between offline and online power distributions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately capturing the three dimensional power distribution within a reactor core is vital for ensuring the safe and economical operation of the reactor, compliance with Technical Specifications, and fuel cycle planning (safety, control, and performance evaluation). Offline (that is, during cycle planning and core design), a three dimensional neutronics simulator is used to estimate the reactor's power, moderator, void, and flow distributions, from which margin to thermal limits and fuel exposures can be approximated. Online, this is accomplished with a system of local power range monitors (LPRMs) designed to capture enough neutron flux information to infer the full nodal power distribution. Certain problems with this process, ranging from measurement and calibration to the power adaption process, pose challenges to operators and limit the ability to design reload cores economically (e.g., engineering in insufficient margin or more margin than required). Artificial intelligence (AI) and machine learning (ML) are being used to solve the problems to reduce maintenance costs, improve the accuracy of online local power measurements, and decrease the bias between offline and online power distributions, thereby leading to a greater ability to design safe and economical reload cores. We present ML models trained from two deep neural network (DNN) architectures, SurrogateNet and LPRMNet, that demonstrate a testing error of 1 percent and 3 percent, respectively. Applications of these models can include virtual sensing capability for bypassed or malfunctioning LPRMs, on demand virtual calibration of detectors between successive calibrations, highly accurate nuclear end of life determinations for LPRMs, and reduced bias between measured and predicted power distributions within the core.

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