Overview of the CORTEX project
Poster (konferens), 2019
Being able to monitor the state of reactors while they are running at nominal conditions would be extremely advantageous. The early detection of anomalies would give the possibility for the utilities to take proper actions before such problems lead to safety concerns or impact plant availability. The analysis of measured fluctuations of process parameters (primarily the neutron flux) around their mean values has the potential to provide non-intrusive on-line core monitoring capabilities. These fluctuations, often referred to as noise, primarily arise either from the turbulent character of the flow in the core, from coolant boiling (in the case of two-phase systems), or from mechanical vibrations of reactor internals. Because such fluctuations carry valuable information concerning the dynamics of the reactor core, one can infer some information about the system state under certain conditions.
A promising but challenging application of core diagnostics thus consists in using the readings of the (usually very few) detectors (out-of-core neutron counters, in-core power/flux monitors, thermocouples, pressure transducers, etc.), located inside the core and/or at its periphery, to backtrack the nature and spatial distribution of the anomaly that gives rise to the recorded fluctuations.
Although intelligent signal processing techniques could also be of help for such a purpose, they would generally not be sufficient by themselves. Therefore, a more comprehensive solution strategy is adopted in CORTEX and relies on the determination of the reactor transfer function or Green’s function, and on its subsequent inversion.
The Green’s function establishes a relationship between any local perturbation to the space-dependent response of the neutron flux throughout the core. In CORTEX, state-of-the-art modelling techniques relying on both deterministic and probabilistic methods are being developed for estimating the reactor transfer function. Such techniques are also being validated in specifically-designed experiments carried out in two research reactors.
Once the reactor transfer is known, artificial intelligence methods relying on machine learning techniques can be used to recover from the measured detector signals the driving anomaly, its characteristics features and location. Some first tests performed within CORTEX on simulated data already demonstrated the viability of this approach.
Chalmers, Fysik, Subatomär fysik och plasmafysik
Core monitoring techniques and experimental validation and demonstration (CORTEX)
Europeiska kommissionen (EU) (EC/H2020/754316), 2017-09-01 -- 2021-08-31.
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