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최형주,천보위,백민규,정희준,정용현,유세환,민철희,최현준 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.6
The purpose of this study was to verify the possibility of fuel rod pattern analysis in a fresh fuel assemblyusing the Yonsei single-photon emission computed tomography (YSECT) system. The YSECT systemconsisted of three main parts: four trapezoidal-shaped bismuth germanate scintillator-based 64-channeldetectors, a semiconductor-based multi-channel data acquisition system, and a rotary stage. In order toassess the performance of the prototype YSECT, tomographic images were obtained for three representative fuel rod patterns in the 6 6 array using two representative image-reconstruction algorithms. The fuel-rod patterns were then assessed using an in-house fuel rod pattern analysis algorithm. In theexperimental results, the single-directional projection images for those three fuel-rod patterns welldiscriminated each fuel-rod location, showing a Gaussian-peak-shaped projection for a single 10 mmdiameter fuel rod with 12.1 mm full-width at half maximum. Finally, we successfully verified the possibility of the fuel rod pattern analysis for all three patterns of fresh fuel rods with the tomographicimages obtained by the rotational YSECT system
이현철,구본탁,전주영,천보위,Yoo Do Hyeon,정희준,민철희 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.10
Radiation portal monitors (RPMs) installed at airports and harbors to prevent illicit trafficking of radioactive materials generally use large plastic scintillators. However, their energy resolution is poor and radionuclide identification is nearly unfeasible. In this study, to improve isotope identification, a RPM system based on a multi-array plastic scintillator and convolutional neural network (CNN) was evaluated by measuring the spectra of radioactive sources. A multi-array plastic scintillator comprising an assembly of 14 hexagonal scintillators was fabricated within an area of 50 × 100 cm2. The energy spectra of 137Cs, 60Co, 226Ra, and 4K (KCl) were measured at speeds of 10–30 km/h, respectively, and an energy-weighted algorithm was applied. For the CNN, 700 and 300 spectral images were used as training and testing images, respectively. Compared to the conventional plastic scintillator, the multi-arrayed detector showed a high collection probability of the optical photons generated inside. A Compton maximum peak was observed for four moving radiation sources, and the CNN-based classification results showed that at least 70% was discriminated. Under the speed condition, the spectral fluctuations were higher than those under dwelling condition. However, the machine learning results demonstrated that a considerably high level of nuclide discrimination was possible under source movement conditions
Implementation of Visible monkey into general-purpose Monte Carlo codes: MCNP, PHITS, and Geant4
이수민,Choi Chansoo,신방호,이유미,최지원,천보위,민철희,정범선,최현준,염연수 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.11
Recently, a new monkey computational phantom, called Visible Monkey, was developed for non-ionizing radiation studies in animal research. In this study, we extended its applications to ionizing radiation studies by implementing the voxel model of the Visible Monkey into three general-purpose Monte Carlo (MC) codes: MCNP6, PHITS, and Geant4. The implementation work for MCNP and PHITS was conducted using the LATTICE, UNIVERSE, and FILL cards. The G4VNestedParameterisation class was used for Geant4. Then, organ dose coefficients (DCs) for idealized photon beams in the antero-posterior direction were calculated using the three codes and compared, showing excellent agreement (differences <3%). Additionally, organ DCs in other directions (postero-anterior, left-lateral, and right-lateral) were calculated and compared with those of the newborn and 1-year-old reference phantoms. Significant differences were observed (e.g., the stomach DC of the monkey was 5-fold greater than that of the 1-year-old phantom at 0.03 MeV) while the differences tended to decrease with increasing energy (mostly <20% at 10 MeV). The results of this study allows conducting MC simulations using the Visible Monkey to estimate organ-level doses, which should be valuable to support/ improve monkey experiments involving ionizing radiation exposures.