• Journal of Radiation Protection and Research
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• v.26 no.1
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• pp.35-43
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• 2001

All of sealing capsules to transport a special form radioactive material should be designed and fabricated in accordance with the design criteria prescribed in IAEA standards and domestic regulations. The objective of this study is to demonstrate the safety of a shipping capsule for $^{192}Ir$ special form radioisotope which produced in the HANARO. The safety tests were carried out for the impact, percussion, bending and heat test conditions. And leakage tests were carried out before and after the each test. Also, the safety analyses wert performed using computer codes in order to verify the test results. The capsule showed slight scratches and deformation, and maintained its structural and thermal integrities in all tests without any severe damage or melting. It also met the allowable limits of leakage rate after each test. Therefore, it has been verified that the capsule was designed and fabricated to meet all requirements for the special form.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.437-443
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• 2024

The purpose of radiological Dispersal Device(RDD) is to kill people by explosives and to cause radiation exposure by dispersing radioactive materials. And It is a form of explosive that combines radioactive materials such as Co-60 and Ir-192 with improvised explosives. In this study, we tested and evaluated whether it was possible to read the internal structure of an explosive using X-rays in a radioactive explosive situation. The improvised explosive device was manufactured using 2 lb of model TNT explosives, one practice detonator, one 9V battery, and a timer switch in a leather briefcase measuring 41×35×10 cm³. The radioactive material used was the Co-60 source used in the low-level gamma ray irradiation device operated at the Advanced Radiation Research Institute of the Korea Atomic Energy Research Institute. The radiation dose used was gamma ray energy of 1.17 MeV and 1.33 MeV from a Co-60 source of 2208 Ci. The dose rates are divided into 0.5, 1, 2, and 4 Gy/h, and the exposure time was divided into 1, 3, 5, and 10 minutes. Co-60 source was mixed with the manufactured explosive and X-ray image reading was performed. As a result of the experiment, the X-ray image appeared black in all conditions divided by dose rate and time, and it was impossible to confirm the internal structure of the explosive. This is because γ-rays emitted from radioactive explosives have higher energy and stronger penetrating power than X-rays, so it is believed that imaging using X-rays is limited By blackening the film. The results of this study are expected to be used as basic data for research and development of X-ray imaging that can read the internal structure of explosives in radioactive explosive situations.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2305-2314
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• 2023

The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an analytic solution. A Gaussian model is simple and enables rapid simulations, but it can be difficult to apply to situations with complex model parameters. Recently, a method of solving PDEs using artificial neural networks called physics-informed neural network (PINN) has been proposed. The PINN assumes the latent (hidden) solution of a PDE as an arbitrary neural network model and approximates the solution by optimizing the model. Unlike a Gaussian model, the PINN is intuitive in that it does not require special assumptions and uses the original equation without modifications. In this paper, we describe an approach to atmospheric dispersion modeling using the PINN and show its applicability through simple case studies. The results are compared with analytic and fundamental numerical methods to assess the accuracy and other features. The proposed PINN approximates the solution with reasonable accuracy. Considering that its procedure is divided into training and prediction steps, the PINN also offers the advantage of rapid simulations once the training is over.