English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Computationally efficient and trustworthy machine learning algorithms arenecessary 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 physicsand 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 nuclearplant operation. In order to address this gap, this study specifically focuseson the "ML-driven prediction algorithms" as a viable component for the nuclearreactor operation while assessing the reliability and efficacy of the proposedmodel. Therefore, as a DT prediction component, this study develops amulti-stage predictive model consisting of two feedforward Deep Learning usingNeural Networks (DNNs) to determine the final steady-state power of a reactortransient for a nuclear reactor/plant. The goal of the multi-stage modelarchitecture is to convert probabilistic classification to continuous outputvariables to improve reliability and ease of analysis. Four regression modelsare developed and tested with input from the first stage model to predict asingle value representing the reactor power output. The combined model yields96% classification accuracy for the first stage and 92% absolute predictionaccuracy for the second stage. The development procedure is discussed so thatthe method can be applied generally to similar systems. An analysis of the rolesimilar models would fill in DTs is performed.

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