Comparison of the SINRD and PDET detectors for the detection of fuel pins diversion in PWR fuel assemblies

摘要

Several non-destructive assay (NDA) techniques are being developed for the measurement of spent nuclear fuel during a safeguards inspection. The development of measurement methods that increase the capabilities of current techniques is a priority in the safeguards community, given that most of the fissile material currently under safeguards is in the form of spent fuel and that additional assemblies continue to be discharged from operating reactors. In the recent years SCK-CEN investigated the use of the Self-Indication Neutron Resonance Densitometry (SINRD) and the Partial Defect Detector (PDET) techniques for the detection of fuel pins diversion in PWR fuel assemblies. The SINRD techniques was initially developed for the direct estimation of the mPu content in a spent fuel assembly by measuring its passive neutron emission in defined energy ranges. The PDET detector was specifically designed for the detection of missing or dummy pins from a PWR fuel assembly by measuring the total neutron and gamma-ray emission in the guide tubes of the assembly. Several diversion scenarios were developed with the Monte Carlo code MCNPX and the results obtained with the SINRD technique were already reported. This contribution stems from this research and compares the responses estimated with Monte Carlo simulations for both SINRD and PDET. In the first phase the responses were calculated for fuel assemblies with different material compositions and source terms. In this way a nominal range of detector responses expected for complete fuel assemblies was obtained. In the second phase a set of diversion scenarios were considered by replacing several fuel pins with dummies. The results show that both techniques achieve similar results for the detection of replaced pins. In particular, the gamma-ray detectors are the most sensitive to the diversions but they are largely influenced by the pins in close proximity to the detector.