11th International Conference on CANDU® Maintenance and Nuclear Components - 2017 Oct. 01-04

Presented at:
11th International Conference on CANDU® Maintenance and Nuclear Components
2017 Oct. 01-04
Toronto, ON Canada
Session Title:
Condition Assessment, Life Assessment and Refurbishment

J.Y. Jung (Korea Atomic Energy Research Institute)


The pressure tube of a CANDU reactor is the most important part of the fuel channel because it passes through the calandria and contains the fuel bundles. However, measurements of the pressure-tube inside diameters at CANDU plants have shown that the diameters have been increasing over time. This phenomenon is due to the effects of irradiation by neutron flux, stress, and reactor operating temperatures over the plant life. This has been regarded as one of the principal aging mechanisms governing the heat transfer and hydraulic degradation of the heat transport system. The diametral expansion of the pressure tube results in the reduction of fuel cooling due to the increased bypass flow, increases the possibility of fuel dryout, and thus limits the operating power of the reactor. Therefore, an accurate prediction of the pressure tube diameter is very important in assessing the operational margin to dryout. This study was focused on the modelling of expansion of the pressure tube diameter based on the operating conditions of the neutron flux and the temperature. It is well known that high temperature and neutron irradiation are the main parameters affecting pressure tube deformation. Accordingly, the pressure tube diameter expansion was modelled using the neutron flux and temperature distribution of each channel and each bundle as well as the measured diametral data of pressure tubes. From the measured data, a creep strain rate was derived for each measured channel and normalized flux and temperature data for each corresponding channel was specifically derived. Although the basic concept of the current modelling approach is simple, prediction results of the pressure tube diameter show very good agreement with the measured data.

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