Enhancing the Operational Resilience of Advanced Reactors with Digital Twins by Recurrent Neural Networks
Because of a lack of operation data during abnormal and accident scenarios, along with the existence of uncertainty in the evaluation model for transient and accident analysis, the established abnormal and emergency operating procedures can be biased in characterizing the reactor states and ensuring operational resilience. To improve state awareness and ensure operational flexibility for minimizing effects on the system due to anomaly, digital twin (DT) technology is suggested to support operator's decision-making by effectively extracting and using knowledge of the current and future plant states from the knowledge base. To demonstrate DT's capability for recovering the complete states of reactors and for predicting the future reactor behaviors, this paper develops and assesses both the diagnosis and prognosis DTs in a nearly autonomous management and control system for an Experimental Breeder Reactor-II simulator during different loss-of-flow scenarios.
- Research Organization:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- Laboratory Directed Research and Development
- DOE Contract Number:
- DE-AC07-05ID14517
- OSTI ID:
- 1827971
- Report Number(s):
- INL/CON-21-64812-Rev000
- Resource Relation:
- Conference: Resilience Week 2021, Virtual, 10/18/2021 - 10/21/2021
- Country of Publication:
- United States
- Language:
- English
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