Detailed models for fluid-induced vibrations of reactor components were generated and a reduced order model was developed. Capabilities were built and tested to simulate reactor neutron noise arising from fuel assembly vibrations with the codes SIMULATE-3, SIMULATE-3K, PARCS, KMACS, FEMFFUSION and CORE SIM+. Neutron noise solvers using higher order approximations in energy, space and angle, were developed, using either probabilistic or deterministic methods. A statistical methodology for uncertainty and sensitivity analysis specific to neutron noise was developed.
The first measurement campaigns at AKR-2 and CROCUS were carried out successfully in 2018, demonstrating the availability of vibrating absorber and absorber of variable strength experimental setups. The data acquisition systems were furthermore qualified against industry-grade equipment. Additional measurements were carried out in 2019 and 2021 for CROCUS and in 2020 and 2021 for AKR-2, based on the feedback received from the code modellers and the experience gained from the early measurement campaigns. A very tight collaboration between the experimentalists and code developers resulted in the validation activities progressing as planned, with a better understanding of the discrepancies between calculations and measurements and in the identification of specific points to be addressed in future measurements.
A large number of simulated data, in frequency and time domains, in related scenarios, were generated to develop and test signal processing methods and machine learning algorithms and architectures. Advanced signal processing methods, based on wavelet and frequency transformations were developed for trend and noise removal and visualization purposes. Novel machine and deep learning methods were successfully applied to the data for predicting perturbation type/location. Very promising results were obtained and published. Extensions to real data were thereafter successfully carried out, with the required modifications of the methods and algorithms.
The simulation tools and signal processing/machine learning methods were then properly merged and applied to actual plant data. For that purpose, four measurement campaigns for additional neutron noise data were prepared and executed at Gösgen NPP. A collection of already available data of KWU, US PWR and VVER reactors was prepared and distributed within the project. Also, core data for steady state calculations were distributed, so that neutron noise simulations for a very large set of postulated scenarios could be executed. Based on those simulation data, the measurement data were pre-processed and fed to the developed machine learning architectures to detect anomalies, classify them, and localize them when relevant. This represented a world-premiere, thus demonstrating the capabilities of the neutron noise-based method developed in CORTEX. An assessment of the effect of uncertainties onto the simulation data and thereafter on the accuracy of the machine-learning based diagnostics was carried out, demonstrating the robustness of the method.
Eight short courses/workshops were arranged on the following topics: signal processing and noise analysis, fundamentals of reactor kinetics and theory of small space-time dependent fluctuations, reactor dynamics, advanced signal processing methods, simulation of neutron noise in power reactors, uncertainty and sensitivity analysis applied to neutron noise calculations, neutron noise measurements and their modelling. The publication records are as follows: journal papers: 15, conference papers: 35, presentations: 23, posters: 5. The following communications channels were developed and regularly updated: website, LinkedIn page, leaflet. A popular science video was also released.