MCNPX simulation and experimental validation of an unmanned aerial radiological system (UARS) for rapid qualitative identification of weak hotspots.

Nuclear threats such as dirty bombs and illicit trafficking of radioactive sources are major concerns of humanity. Fast detection and accurate localization of radioactive material out of regulatory control (MORC) by autonomous and semi-autonomous monitoring systems like robots can help to reduce radiation exposure to the public and workers, and it will improve security and peace in the world. This study proposes an autonomous radiological monitoring system consisting of a 2-inch NaI detector coupled to a PM tube and mounted on a multi-rotor UAV to detect radioactive sources. First, an experimental scenario was modeled using the MCNPX Monte Carlo (MC) code. In this modeling, the gamma spectra in 15 detectors were recorded from the rays emitted simultaneously from the areas' sources. The total count under the spectrum was measured for each of the detectors at different heights. The experimental tests were also performed to detect the simultaneous effect of five low-level Co-60 and Cs-137 point sources on a soccer field. Next, the modeling results were compared with the experimental ones, which showed good agreement and the capability to use MC modeling to simulate different radiological scenarios. The experimental results also showed that at 50 cm, all radioactive sources were successfully detected in their actual location. By decreasing the flight height, the ability of the monitoring unmanned aerial to detect radioactive sources was increased significantly.

Detecting and tracking multiple mobile radioactive sources by data fusion of a surveillance camera and a sodium iodide (NaI) detector.

Nowadays, the use of radioactive materials has increased due to recent progress in nuclear technology, but concerns about the missing or smuggling of radioactive materials have persisted. Nuclear threats such as terrorist attacks or the malicious use of radioactive materials out of regulatory control (MORC) are always severe problems that can result in adverse environmental, health, economic, and security effects. Applying new technologies to monitor and track MORC will prevent radioactive materials from being illegally transported across borders or from one place to another. In this research, we have constructed a detection system to automatically and remotely localize multiple mobile gamma-emitting radiation sources among other objects by combining an IP camera and a sodium iodide detector. An algorithm for the detection system has been developed to identify the objects' paths from camera data and correlate radiation data with the paths to detect contaminated objects. We evaluated the system using two weak radioactive sources (Co-60, Cs-137), which were hidden on moving objects, and succeeded in finding harmful targets. The results have also shown that by increasing the number of contaminated sources from one to two, the detection accuracy decreases by about 2.5 times. However, by doubling the speed of mobile targets, the detection accuracy improved by about 30%. Detecting and tracking MORC with a single detection system is limited to small regions. By equipping many surveillance cameras in a city with relatively inexpensive radioactive detectors, a network sensing system is established to find radioactive hotspots in a smart city.