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

Author:

Karnik Niharika1ORCID,Wang Congjian2ORCID,Bhowmik Palash K.2ORCID,Cogliati Joshua J.2,Balderrama Prieto Silvino A.2ORCID,Xing Changhu2,Klishin Andrei A.1ORCID,Skifton Richard2ORCID,Moussaoui Musa2,Folsom Charles P.2ORCID,Palmer Joe J.2ORCID,Sabharwall Piyush2ORCID,Manohar Krithika1ORCID,Abdo Mohammad G.2ORCID

Affiliation:

1. Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA

2. Idaho National Laboratory, Idaho Falls, ID 83415, USA

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.

Funder

United States Department of Energy

Publisher

MDPI AG

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