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

TL;DR

This paper develops LSTM models to predict and forecast reactivity and flux in pebble bed reactors during operation, addressing measurement challenges and enabling optimization of run-in procedures.

Contribution

It introduces a novel application of LSTM networks for real-time prediction and forecasting of reactor parameters based on operational history and synthetic measurements.

Findings

Achieved an R^2 of 0.9914 in predicting reactivity and flux.

Demonstrated capability to forecast reactivity responses to future changes.

Explored application for optimizing reactor run-in procedures.

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.