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arXiv · 2511.05118

Predicting and forecasting reactivity and flux using long short-term memory models in pebble bed reactors during run-in

Ian Kolaja·Ludovic Jantzen·Tatiana Siaraferas·Massimiliano Fratoni
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Abstract

Pebble bed reactor (PBR) operation presents unique advantages and challenges due to the ability to continuously change the fuel mixture and excess reactivity. Each operation parameter affects reactivity on a different timescale. For example, fuel insertion changes may take months to fully propagate, whereas control rod movements have immediate effects. In-core measurements are further limited by the high temperatures, intense neutron flux, and dynamic motion of the fuel bed. In this study, long short-term memory (LSTM) networks are trained to predict reactivity, flux profiles, and power profiles as functions of operating history and synthetic batch-level pebble measurements, such as discharge burnup distributions. The model's performance is evaluated using unseen temporal data, achieving an $R^2$ of 0.9914 on the testing set. The capability of the network to forecast reactivity responses to future operational changes is also examined, and its application for optimizing reactor running-in procedures is explored.

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Ian Kolaja, Ludovic Jantzen, Tatiana Siaraferas, Massimiliano Fratoni. 2025-11-07. Predicting and forecasting reactivity and flux using long short-term memory models in pebble bed reactors during run-in. https://arxiv.org/abs/2511.05118

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