First-of-a-Kind Risk-Informed Digital Twin for Operational Decision Making

Michael D. Muhlheim, Pradeep Ramuhalli, Askin Guler Yigitoglu, Alex Huning, Richard Wood, Jorge L. Narvaez, Abhinav Saxena

Research output: Contribution to journalArticlepeer-review

Abstract

A digital twin (DT) is a digital model or a collection of models of a physical entity. DTs in the nuclear arena can be used from plant design through decommissioning. Decisions are typically a priori or made offline. Risk-informed decision making is identifying what can go wrong, its frequency, and the consequences of its failure. Ideally risk-informed decision making reflects the current state of the plant and provides a decision in real time. Traditionally, probabilistic risk assessments (PRAs) evaluate the failures of safety systems, the risk of core damage, and the offsite dose as the consequence. However, this DT evaluates the decisions on the control side rather than the protection side. It uses the same risk methods to probabilistically inform the decision-making process but in a different way. Rather than evaluating the risk of core damage, this DT evaluates the likelihood of avoiding a trip set point while maintaining plant safety. Performance-based assessments are identified via its probabilistic evaluation of operational alternatives based on system status. Because the purpose of the control system is to maintain system variables within prescribed operating ranges, upsets or challenges that can exceed a trip set point resulting in a plant transient and a challenge to plant mitigating systems based on actual plant conditions, are evaluated to safely maintain the plant within the operating ranges. The probabilistic portion of the model is autonomously and automatically adjusted, and the metric of interest (i.e. likelihood of avoiding a trip set point) is recalculated. The digital representation of the physical system (i.e. the DT) performs a deterministic performance–based assessment of the probabilistically identified alternatives identified to validate the probabilistic assessment. A decision-making algorithm selects the appropriate option based on the probabilistic and deterministic assessments and transmits a control signal to a component(s) to initiate a corrective action or informs an operator of its decision.

Original languageEnglish
Pages (from-to)1899-1914
Number of pages16
JournalNuclear Science and Engineering
Volume199
Issue number11
DOIs
StatePublished - 2025

Funding

The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E) of the U.S. Department of Energy under award number DE-AR0001290. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the U.S. government or any agency thereof.

Keywords

  • Digital twin
  • risk-informed
  • risk-informed decision making

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