Neutron Flux Reconstruction from Out-Core Sparse Measurements using Data-Driven Reduced Order Modelling

For Generation-IV nuclear reactors, the problem of optimal sensor positioning and real-time estimation of the quantities of interest is an open problem. In particular, the harsh environment of fast reactors, both due to the high radioactive levels and the presence of non-conventional coolants such as liquid metals or molten salts, is such that in-core sensor positioning requires careful attention. This problem is especially true for the Molten Salt Fast Reactor (MSFR), which foresees fuel and coolant homogeneously mixed in the liquid phase and whose current design does not envision in-core structures. Thus, the possibility of estimating relevant in-core quantities, such as the neutron flux, from measurements taken outside the reactor core, for example, by sensors located in the reflector, is worth exploring, as it has important implications for safety, monitoring and control. In this context, the Data-Driven Reduced Order Modelling framework offers a promising tool for combining out-core sparse measurements with some mathematical background knowledge, in the form of a reduced-order model, on the in-core state to efficiently and accurately reconstruct the latter in the whole core domain. This work explores this possibility by employing the Generalised Empirical Interpolation Method (GEIM) to retrieve the in-core neutron flux starting from sparse out-core noisy measurements of this quantity, including a preliminary step of optimisation of the sensor positioning in the reflector surrounding the core.