Acknowledgement
본 연구는 미래창조과학부 지원으로 수행되었습니다(NRF-2015M2A8A4047136).
In ports and airports, radiation portal monitors (RPM) are deployed to detect illicit radioactive materials. Detected gamma rays in a RPM include background radiation and radiation from a freight. As a vehicle moves through the RPM, the vehicle causes the fluctuations in the natural background radiation signal, which ranges of up to 30%. The fluctuation increases the uncertainty of detection signal and can be a cause of RPM false alarm. Therefore, it is important to evaluate background radiation as well as radiation from a container. In this paper, a natural background radiation model was developed to evaluate RPM. To develop natural background radiation model, a Monte Carlo simulation was performed and compared with experimental measurements from a RPM for 40K, 232Th series, and 235U series, which are major sources of natural background radiation. For a natural radiation source, we considered a cylindrical soil volume with 300 m radius and 1 m depth, which was estimated as the maximum range affecting the RPM by MCNP6 simulation. The volume source model was converted to surface source by using MCNP SSW card for computational efficiency. The computational efficiency of the surface source model was improved to approximately 200 times better than that of the volume source model. The surface source model is composed of a hemisphere with 20 m radius in which the RPM and container are modelled. The natural radiation spectrum from the simulation was best fitted to the experimental measurement when portions of 40K, 232Th series, and 235U series were 0.75, 0.0636, and 0.0552 Bq·g-1, respectively. For gross counting results, the difference between simulation and experiment was around 5%. The background radiation model was used to evaluate background suppression from a 40 ft container with 7.2 m·s-1 speed. In further study, background models and freight models for RPM in real container ports will be developed and applied to optimize the operating system of port security.
본 연구는 미래창조과학부 지원으로 수행되었습니다(NRF-2015M2A8A4047136).