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Can I read Development and Assessment of a Reactor System Prognosis Model with Physics-guided Machine Learning on EtoBox?
Development and Assessment of a Reactor System Prognosis Model with Physics-guided Machine Learning by Anil Gurgen; Nam T. Dinh is a Engineering article available to read on EtoBox.
What is Development and Assessment of a Reactor System Prognosis Model with Physics-guided Machine Learning about?
Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relations
Who reads Development and Assessment of a Reactor System Prognosis Model with Physics-guided Machine Learning?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Anil Gurgen; Nam T. Dinh
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Engineering (Physical Sciences)