Modeling of Boiling Water Reactor Feedwater Control Algorithms

Mowrey, J. A.; Boylan, P. R.; Abdel‐Khalik, S. I.

doi:10.13182/nt98-a2922

Cited by 2 publications

(1 citation statement)

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“…Modelbased control systems rely on some underlying model that is reasonably predictive of the system dynamics. Several examples of model based control exist for nuclear power systems [52][53][54][55], and are generally applied to the core power, but may be used for other components or quantities of interest [56,57]. These models are typically lightweight so as to meet requirements for real-time execution and may be constructed rigorously through physics-based methods, statistical methods, purely data-driven approaches, or ML.…”

Section: Model Based Controllersmentioning

confidence: 99%

Digital Twin Concepts with Uncertainty for Nuclear Power Applications

Kochunas

Huan

2021

Energies

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Digital Twins (DTs) are receiving considerable attention from multiple disciplines. Much of the literature at this time is dedicated to the conceptualization of digital twins, and associated enabling technologies and challenges. In this paper, we consider these propositions for the specific application of nuclear power. Our review finds that the current DT concepts are amenable to nuclear power systems, but benefit from some modifications and enhancements. Further, some areas of the existing modeling and simulation infrastructure around nuclear power systems are adaptable to DT development, while more recent efforts in advanced modeling and simulation are less suitable at this time. For nuclear power applications, DT development should rely first on mechanistic model-based methods to leverage the extensive experience and understanding of these systems. Model-free techniques can then be adopted to selectively, and correctively, augment limitations in the model-based approaches. Challenges to the realization of a DT are also discussed, with some being unique to nuclear engineering, however most are broader. A challenging aspect we discuss in detail for DTs is the incorporation of uncertainty quantification (UQ). Forward UQ enables the propagation of uncertainty from the digital representations to predict behavior of the physical asset. Similarly, inverse UQ allows for the incorporation of data from new measurements obtained from the physical asset back into the DT. Optimization under uncertainty facilitates decision support through the formal methods of optimal experimental design and design optimization that maximize information gain, or performance, of the physical asset in an uncertain environment.

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