Detecting hidden/smuggled special nuclear materials (SNM) is one of the unsolved problems in the safeguards industry. The biggest challenge is to quantify and track SNM and prevent the use of these materials for illicit purposes. The goal is to detect smallest quantity of SNM in large cargo containers, at the ports of entry, in the shortest amount of measurement time. Currently passive detection techniques, which is based on long-lived isotopes, are used to detect hidden SNM. This technique is not very reliable, as appropriate shielding of the SNM can reduce detection signals from these long-lived isotopes. Accelerator based active interrogation methods are proposed to solve the SNM problem. Besides SNM, another challenge in the nuclear industry is to meet the demand and supply of medical radioisotopes, particularly Tc-99m (half-life 6 hours). Mo-99, which decays to Tc-99m, is one of the fission products found in nuclear reactors. Because of short half-life of 66 hours, Mo-99 cannot be stockpiled. The shutdown of various research reactors globally disrupted the supply of Mo-99. Because of the financial and regulatory burden on the nuclear reactors, accelerator-based systems can be used to produce Mo-99.
With the aim to solve these two major challenges, a preliminary study is done to understand the neutrons behavior on milliseconds (or shorter) time steps in an accelerator driven subcritical system. A pulsed external neutron source, i.e. Deuterium-Deuterium (DD) generator, drives the assembly. Using first principles, the transient equations are derived and the neutron population at different time stamps is calculated. The Li-6 detector’s response to the neutron population is predicted. Experiments are performed to compare the predicted behavior with the observed behavior. The model is extended further to investigate the case of no uranium fuel inside the system. Transient measurements, in the absence of the uranium fuel, are made and the neutron die-away time is determined. This die-away time is compared with the predicted time.