Given harsh operating conditions and physical constraints in reactors,
nuclear applications cannot afford to equip the physical asset with a large
array of sensors. Therefore, it is crucial to carefully determine the placement
of sensors within the given spatial limitations, enabling the reconstruction of
reactor flow fields and the creation of nuclear digital twins. Various design
considerations are imposed, such as predetermined sensor locations, restricted
areas within the reactor, a fixed number of sensors allocated to a specific
region, or sensors positioned at a designated distance from one another. We
develop a data-driven technique that integrates constraints into an
optimization procedure for sensor placement, aiming to minimize reconstruction
errors. Our approach employs a greedy algorithm that can optimize sensor
locations on a grid, adhering to user-defined constraints. We demonstrate the
near optimality of our algorithm by computing all possible configurations for
selecting a certain number of sensors for a randomly generated state space
system. In this work, the algorithm is demonstrated on the Out-of-Pile Testing
and Instrumentation Transient Water Irradiation System (OPTI-TWIST) prototype
vessel, which is electrically heated to mimic the neutronics effect of the
Transient Reactor Test facility (TREAT) at Idaho National Laboratory (INL). The
resulting sensor-based reconstruction of temperature within the OPTI-TWIST
minimizes error, provides probabilistic bounds for noise-induced uncertainty
and will finally be used for communication between the digital twin and
experimental facility