  The development of Crud-Induced Power Shift (CIPS) is an operational
challenge in Pressurized Water Reactors that is due to the development of crud
on the fuel rod cladding. The available predictive tools developed previously,
usually based on fundamental physics, are computationally expensive and have
shown differing degrees of accuracy. This work proposes a completely top-down
approach to predict CIPS instances on an assembly level with reactor-specific
calibration built-in. Built using artificial neural networks, this work uses a
three-dimensional convolutional approach to leverage the image-like layout of
the input data. As a classifier, the convolutional neural network model
predicts whether a given assembly will experience CIPS as well as the time of
occurrence during a given cycle. This surrogate model is both trained and
tested using a combination of calculated core model parameters and measured
plant data from Unit 1 of the Catawba Nuclear Station. After the evaluation of
its performance using various metrics, Monte Carlo dropout is employed for
extensive uncertainty quantification of the model predictions. The results
indicate that this methodology could be a viable approach in predicting CIPS
with an assembly-level resolution across both clean and afflicted cycles, while
using limited computational resources.
