Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Niharika Karnik (), Congjian Wang, Palash K. Bhowmik, Joshua J. Cogliati, Silvino A. Balderrama Prieto, Changhu Xing, Andrei A. Klishin, Richard Skifton, Musa Moussaoui, Charles P. Folsom, Joe J. Palmer, Piyush Sabharwall, Krithika Manohar () and Mohammad G. Abdo ()
Additional contact information
Niharika Karnik: Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
Congjian Wang: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Palash K. Bhowmik: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Joshua J. Cogliati: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Silvino A. Balderrama Prieto: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Changhu Xing: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Andrei A. Klishin: Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
Richard Skifton: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Musa Moussaoui: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Charles P. Folsom: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Joe J. Palmer: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Piyush Sabharwall: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Krithika Manohar: Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
Mohammad G. Abdo: Idaho National Laboratory, Idaho Falls, ID 83415, USA

Energies, 2024, vol. 17, issue 13, 1-28

Abstract: Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

Keywords: digital twins; sensor placement; data-driven; nuclear power plants; modeling and simulations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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