Generating and Simulating CANDU® Fuel Channel Eddy Current Data to Demonstrate Multi-Parameter Extraction Capabilities

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Purdy, Owen
CANDU , Eddy Current , Pressure Tube , Calandria Tube , Gap
An eddy current probe known as the gap probe is used in CANDU® (CANadian Deuterium Uranium) nuclear reactors to verify that Pressure Tube to Calandria Tube (PT-CT) gap in Fuel Channels is larger than the minimum requirement. Contact between the two tubes can severely reduce the structural integrity of the PT. The measurement is affected by nearby conducting structures, which include Liquid Injection Shutdown System (LISS) Nozzles, Garter Spring (GS) spacers and the tooling body itself, which is shielded by a flat copper plate. The electrical properties of the PT and its distance from the face of the gap probe also impact the measurement. Generating and simulating data, which accounts for these factors enable changes in the gap probe eddy current response from PT-CT gap variations to be isolated from variations caused by the other conducting structures and parameters of interest. This thesis presents an analytical model of the eddy current gap probe response with the addition of a copper plate. This analytical model was shown to have excellent agreement with simulation data from a validated Finite Element Method (FEM) model. Efforts made to detect tight-fitting GSs with disconnected girdle wires are also presented. This method of detection was determined as not being feasible, as there are insufficient eddy currents induced into the conducting volume of the GS connector. A validated FEM model, which can simulate the change in receive coil voltage measured by the gap probe caused by a nearby LISS Nozzle, is developed. The FEM model displayed excellent agreement with experimental data. A Deep Neural Network (DNN) was implemented to simultaneously extract PT-CT gap, and LISS-CT distance from experimental PT-CT gap inspection data. The extraction of PT-CT gap in the proximity of LISS nozzles has not previously been demonstrated. It was also shown that a DNN could be used to simultaneously predict PT-CT gap, PT wall thickness, PT resistivity, and probe liftoff from experimental validation data. The demonstration of this capability is of significant value to nuclear utilities as DNNs could be used for other types of similar inspection applications.
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