Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction

by   James Daniell, et al.

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 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.


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