Deep heterogeneous joint architecture: A temporal frequency surrogate model for fuel performance codes

Fuel performance codes, such as Transuranus, predict fuel behavior and are used to ensure the safe operation of nuclear reactors. These codes are moderately time-consuming and affordable in many applications but may be limited in others, primarily when many fuel rods must be evaluated simultaneously. This work presents how the temporal neural network techniques, Temporal Convolutional Networks, and a Fourier Neural Operator can be combined to form a deep heterogeneous joint architecture as a surrogate model for fuel performance modeling in time-critical situations. We train the model using realistic power histories and corresponding outputs generated using the fuel performance code Transuranus. The ultimate result is a surrogate model for use in time-critical situations that take milliseconds to evaluate for thousands of fuel rods and have a mean test error of unseen data around a few percent. • TFN, a joint neural network architecture for temporal data. • Used to map power histories to fuel performance predictions. • Trained on data generated by the Transuranus fuel performance code. • Rapid, accurate modeling of multiple fuel rods in time-critical situations.