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

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

Increased burn severity and simulated climate warming drive shifts in ecosystem function and understory plant traits one year post-fire in northern Arizona

A changing climate and altered fire regimes in the semi-arid southwestern U.S. have led to questions regarding future forest composition and ecosystem processes. Forests that are fire adapted and have been historically shaped by fire are at risk of conversion to non-forest ecosystems by uncharacteristically severe fires. Trait-based plant ecology provides a means of assessing the impacts of varying levels of disturbance on community composition and ecosystem function and can be used to predict community trajectories. The aim of this study was to quantify the effects of increasing burn severity and simulated climate warming on two ecosystem functions and three plant functional traits, approximately one year post-fire in a northern Arizona Pinus ponderosa forest understory. Plots were established along a burn severity gradient including unburned, low, and high burn severity areas. Open-top warming chambers were employed to produce just over 1°C of warming. We found that the interaction of high burn severity and simulated climate warming significantly increased decomposition rates, decreased community weighted mean (CWM) trait values for leaf dry matter content, and increased specific leaf area of a dominant C4 bunchgrass (Muhlenbergia montana). Additional significant effects were observed according to severity alone. Our results indicate potential for divergent community composition, structure, and ecosystem functioning over time in response to disturbance and warming. Our results indicate that herbaceous and shrub understory communities after future high severity fires may be on novel successional trajectories. Our conclusions support ongoing efforts to reduce the likelihood of high severity fires in this region, and provide information on trait expressions and species that may prove useful in future post-fire restoration efforts. Given the projected increase of high severity fires in the southwestern U.S., understanding the combined effects of climate warming and high burn severity will be critical for successful proactive land management