Sensitivity analysis in core diagnostics. (1st December 2022)

• Record Type: Journal Article
• Authors: Herb, J.; Périn, Y.; Yum, S.; Mylonakis, A.; Demazière, C.; Vinai, P.; Yu, M.; Wingate, J.; Hursin, M.
• Is Part Of: Annals of nuclear energy. Volume 178(2022)
• Publication Date: 2022-12-01
• Subjects: Core diagnostics -- Sensitivity analysis -- Neutron flux noise -- Nuclear data -- Deep neural networks; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2022.109350
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Comprehensive sensitivity analysis of neutron flux calculation method. Analysis of the robustness of a deep neural network to identify perturbations. The phase of the neutron flux oscillations is sensitive to variations. The most important parameters were the location and amplitude of the noise source. The neural network identifies cause and location of perturbations very robustly. Abstract: In the CORTEX project, methods to simulate neutron flux oscillations were enhanced and machine-learning based tools to determine the causes of measured neutron flux oscillations were developed, using the results of simulations as training and validation data. For a selected combination of those methods and tools, several sensitivity analyses were performed to assess their robustness and trustworthiness. The neutron flux oscillations were simulated using the tool CORE SIM+. It calculates the three-dimensional field of the neutron flux oscillations, which can be used to determine the response of neutron detectors at given locations. For the sensitivity analysis, the neutron flux oscillations were assumed to be caused by the vibration of one fuel element. It was investigated how selected input parameters like the core loading pattern, the burn up of the fuel elements, the neutronic core data, the geometry details of the vibrating fuel element, the chosen detectors, and other noise source parameters like the amplitude of the fuel element vibrations, affect the simulated neutron flux oscillations. A three dimensional fully convolutional neural network had been developed and trained during the CORTEX project to determine the cause and location of perturbations causing given measurements of in-core detectors in pressurized water reactors. The robustness of this network was tested by applying it to the simulated detector readings created during the sensitivity analysis.