Machine Learning, Dynamical Systems and Control

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Idaho National Laboratories (INL)

Promoting Optimal Sparse Sensing and Sparse Learning for Nuclear Digital Twins

In collaboration with INL, we are optimizing sensor placements for nuclear digital twins given spatial constraints and hostile operating conditions for sensor installation. In this setting, sensors critically enable communication between the virtual/digital twin (ROMs and simulations) of the Transient Water Irradiation System in TREAT (TWIST) prototype, and the experimental/physical facility. The TWIST prototype is a multi-purpose test rig that will simulate transient loss of coolant, out-of-pile, to study thermal-hydraulics behavior of an identical irradiation rig for the INL Transient Reactor Test Facility (TREAT). Our algorithms, equipped with physics simulations and ROMs at the design stage, are extended to adaptively optimize sensors with emerging spatial constraints imposed by the environment. The resulting sensor-based reconstructions of reactor flow fields minimize error, provide probabilistic estimates of noise-induced uncertainty, and ultimately will be used for robust monitoring and control of the TWIST Capsule.