  A challenging part of dynamic probabilistic risk assessment for nuclear power
plants is the need for large amounts of temporal simulations given various
initiating events and branching conditions from which representative feature
extraction becomes complicated for subsequent applications. Artificial
Intelligence techniques have been shown to be powerful tools in time-dependent
sequential data processing to automatically extract and yield complex features
from large data. An advanced temporal neural network referred to as the
Transformer is used within a supervised learning fashion to model the
time-dependent NPP simulation data and to infer whether a given sequence of
events leads to core damage or not. The training and testing datasets for the
Transformer are obtained by running 10,000 RELAP5-3D NPP blackout simulations
with the list of variables obtained from the RAVEN software. Each simulation is
classified as "OK" or "CORE DAMAGE" based on the consequence. The results show
that the Transformer can learn the characteristics of the sequential data and
yield promising performance with approximately 99% classification accuracy on
the testing dataset.
