• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.1
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• pp.123-136
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• 2018

The objective of this study is to propose a new methodology to fabricate a reliable engineering-scale buffer block, which shows homogeneous and uniform distribution in buffer block density, for in-situ experiments. In this study, for the first time in Korea, floating die press and CIP (Cold Isostatic Press) are applied for the manufacture of an engineering-scale bentonite buffer. The optimized condition and field applicability are also evaluated with respect to the method of manufacturing the buffer blocks. It is found that the standard deviation of the densities obtained decreases noticeably and that the average dry density increases slightly. In addition, buffer size is reduced by about 5% at the same time. Through the test production, it is indicated that the stress release phenomenon decreases after the application of the CIP method, which leads to a reduction in crack generation on the surface of the buffer blocks over time. Therefore, it is confirmed that the production of homogeneous buffer blocks on industrial scale is possible using the method suggested in this study, and that the produced blocks also meet the design conditions for dry density of buffer blocks in the AKRS (Advanced Korea Reference Disposal System of HLW).

• Tunnel and Underground Space
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• v.30 no.6
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• pp.509-518
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• 2020

This study constructed a new simulation platform, including mesh generation process, numerical simulation, and post-processing for results analysis based on exploration data to perform real-scale numerical analysis considering the actual geological structure efficiently. To build the simulation platform, we applied for open-source programs. The source code is open to be available for code modification according to the researcher's needs and compatibility with various numerical simulation programs. First, a three-dimensional model(3D) is acquired based on the exploration data obtained using a drone. Then, the domain's mesh density was adjusted to an interpretable level using Blender, the free and open-source 3D creation suite. The next step is to create a 3D numerical model by creating a tetrahedral volume mesh inside the domain using Gmsh, a finite element mesh generation program. To use the mesh information obtained through Gmsh in a numerical simulation program, a converting process to conform to the program's mesh creation protocol is required. We applied a Python code for the procedure. After we completed the stability analysis, we have created various visualization of the study using ParaView, another open-source visualization and data analysis program. We successfully performed a preliminary stability analysis on the full-scale Dokdo model based on drone-acquired data to confirm the usefulness of the proposed platform. The proposed simulation platform in this study can be of various analysis processes in future research.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.