  Computationally efficient and trustworthy machine learning algorithms are
necessary for Digital Twin (DT) framework development. Generally speaking,
DT-enabling technologies consist of five major components: (i) Machine learning
(ML)-driven prediction algorithm, (ii) Temporal synchronization between physics
and digital assets utilizing advanced sensors/instrumentation, (iii)
uncertainty propagation, and (iv) DT operational framework. Unfortunately,
there is still a significant gap in developing those components for nuclear
plant operation. In order to address this gap, this study specifically focuses
on the "ML-driven prediction algorithms" as a viable component for the nuclear
reactor operation while assessing the reliability and efficacy of the proposed
model. Therefore, as a DT prediction component, this study develops a
multi-stage predictive model consisting of two feedforward Deep Learning using
Neural Networks (DNNs) to determine the final steady-state power of a reactor
transient for a nuclear reactor/plant. The goal of the multi-stage model
architecture is to convert probabilistic classification to continuous output
variables to improve reliability and ease of analysis. Four regression models
are developed and tested with input from the first stage model to predict a
single value representing the reactor power output. The combined model yields
96% classification accuracy for the first stage and 92% absolute prediction
accuracy for the second stage. The development procedure is discussed so that
the method can be applied generally to similar systems. An analysis of the role
similar models would fill in DTs is performed.
