Position and Anomaly Sensitivity for Multi-Variant Signature Analysis Utilizing a Natural Uranium Fresh Fuel Mock-Up

The nuclear community does not currently employ a multi-variant detector system to discern between fresh nuclear fuel assemblies. This research examined a detection system consisting of four commercial off-the-shelf detectors: He-3 proportional counter, NaI(Tl) crystal scintillator, Eljen EJ-309 liquid scintillator, and $\mathrm{Cs}_{2} \mathrm{LiYCl}_{6}: \mathrm{Ce}^{3+}$ (CLYC) crystal scintillator; these detectors are capable of measuring either gammas and/or neutrons and this research applied the system against a natural uranium fresh fuel mock-up. The natural uranium fresh fuel mock-up was designed in CAD and machined out of aluminum. It is designed for multiple configuration variations; for this work it was adapted to ten different configurations of natural uranium. The detection system conducted measurements at three positions. Data was post-processed to be used to develop an initial machine learning algorithm capable of discerning between natural uranium fresh fuel mockup configurations. This algorithm will be modified and further developed in future experiments. It provides initial promise at successfully radiologically verifying and distinguishing between fresh nuclear fuel assemblies.