Climate change has led to a significant increase in jellyfish populations globally, causing various problems. For power plants that use nearby seawater for cooling, the intrusion of jellyfish into intake systems can block the flow, leading to reduced output or even shutdowns. This issue is compounded by other small marine organisms like shrimp and salps, making it urgent to develop solutions to prevent their intrusion. This study addressed the problem using the BioSonics DT-X 120 kHz scientific fish finder to conduct preliminary tank experiments. We also deployed underwater acoustic and camera buoys around the intake of nuclear power plant, utilizing a bidirectional communication system between sea and land to collect data. Data collection took place from July 31, 2023 to August 1, 2023. While harmful organisms such as jellyfish and salps were not detected, we successfully gathered acoustic data on small fish measuring backscattering strength (SV). Analysis showed that fish schools were more prominent in the evening than during the day. The highest fish distribution was observed at 3:30 AM on July 31 with an SV of -44.8 dB while the lowest was at 12:30 PM on the same day with an SV of –63.4 dB. Additionally, a solar-powered system was used to enable real-time data acquisition from sea buoys with smooth communication between the land server and the offshore buoy located 1.8 km away. This research developed an acoustic-based monitoring system for detecting harmful organisms around the intake and provided foundational data for preventing marine organism intrusion and planning effective measures.

To construct and operate nuclear power plants (NPPs), it is mandatory to submit a radiation environmental impact assessment report in accordance with Article 10 and Article 20 of the Nuclear Safety Act. Additionally, in compliance with Article 136 of the Enforcement Regulations of the same law, KHNP (Korea Hydro & Nuclear Power) annually assesses radiation environmental effects and publishes the results for operating NPPs. Furthermore, since the legalization of emission plans submission in 2015, KHNP has been submitting emission plans for individual NPPs, starting with the Shin-Hanul 1 and 2 units in 2018. These emission plans specify the emission quantities that meet the dose criteria specified by the Nuclear Safety and Security Commission. Before 2002, KHNP used programs developed in the United States, such as GASPAR and LADTAP, for nearby radiation environmental impact assessments. Since then, KHNP has been using K-DOSE60, developed internally. K-DOSE60 incorporates environmental transport analysis models in line with U.S. regulatory guidance Regulatory Guide 1.109 and dose assessment models reflecting ICRP-60 recommendations. K-DOSE60 is a stand-alone program installed on individual user PCs, making it difficult to manage comprehensively when program revisions are needed. Additionally, during the preparation of emission plans and the licensing phase, improvements to KDOSE60’ s dose assessment methodology were identified. Furthermore, in 2022, regulatory guidelines regarding resident dose assessments were revised, leading to additional improvement requirements. Currently, E-DOSE60, being developed by KHNP, is a network-based program allowing for integrated configuration management within the KHNP network. E-DOSE60 is expected to be developed while incorporating the identified improvements from K-DOSE60, in response to emission plan licensing and regulatory guideline revisions. Key improvements include revisions to dose assessment methodologies for H-13 and C-14 following IAEA TRS-472, expansion of dose assessment points, and changes in socio-environmental factors. Furthermore, data such as site meteorological information and releases of radioactive substances in liquid and gaseous forms can be linked through a network, reducing the potential for human errors caused by manual data entry. Ultimately, E-DOSE60 is expected to optimize resident exposure dose assessment and enhance public trust in NPP operation.

In the dismantling of nuclear power plants, various forms of radioactive gaseous waste are generated when cutting concrete and metal structures. Large amounts of radioactive dust and aerosols generated during the cutting process of each structure can cause radiation exposure to the environment around the workplace and to the radiation exposure in the body of workers. When cutting structures, water is sprayed to reduce the generation of aerosols, so early saturation of the filter is expected due to radioactive aerosols and fine particles containing a large amount of moisture. A mobile air purification device is being developed to a fast and efficient air purifier that can be used for a long time operation to protect workers from radiation exposure in high radiation areas and to minimize the amount of secondary waste generated. In this paper, the direction for a new concept of unit technology that can achieve the development purpose is described.

The decommissioning of domestic Nuclear Power Plants (NPPs) in Korea is expected to begin with the Kori-1, which was permanently shutdown in 2017. In addition, Wolsong-1 has been also permanently shutdown, and another type will be the decommissioning project following Kori-1. KHNP is promoting operation and decommissioning projects as the owner of NPPs, and the Central Research Institute (CRI) has been developing a Final Decommissioning Plan (FDP) for the decommissioning license document. The FDP consists of 11 major chapters in the order of overview of the project, characteristic evaluation, safety assessment, radiation protection, decontamination & dismantlement activities, waste management, etc. The contents described in each chapter are individual chapters, but there are also parts that consider the connection with other chapters. The CRI, which develops the FDP for the first decommissioning project in Korea, has spent a lot of time and effort considering this and has been proceeding through trial and error until the present stage. Therefore, this study aims to explain the current status of FDP, a license document for domestic decommissioning projects, and the link between major input data in major chapters. It can be said that System, Structure, and Components (SSCs) subject to dismantling are considered as the scope of FDP. Chapters that perform estimations on these dismantling targets may include safety assessments, exposure dose assessments for workers and residents, and waste inventory assessments. Therefore, an important part of performing the estimation works is to consider the entire scope of decommissioning activities, and as a way, it can start from data based on the inventory data. After generating the inventory data, the waste treatment classification for the inventory is designated by reflecting the results of the characterization. In addition, for cost estimation, the cost of decommissioning project is predicted by inputting some data (i.e., UCF) such as work process, number of workers, and time required for each item with data reflected in quantity and characterization. After that, based on these inventory, characterization, and UCF data, accident scenarios and industrial safety evaluation are performed for the safety assessment. The worker exposure dose is estimated by considering the dose rate of the workspace with these data. In the case of the amount of waste, the final amount of waste is estimated by considering the factors of reduction and decontamination. In summary, the main estimation contents of FDP are evaluated by adding elements required for the purpose of each chapter from data combined with inventory, characterization, and UCF, so the contents of these chapters are based on the logic of considering the entire scope of decommissioning in common.

The seven-year research project entitled “Development of workflow for integrated 3D geological site descriptive modeling” is being carried out from 2023. This research is funded by Ministry of Trade, Industry, and Energy (MOTIE). Progress of the research is discussed here. The integrated 3D geological SDM (site descriptive model; GSDM hereafter) consists of three part; 1) three dimensional representation of geologic elements, 2) database for material properties and modeling results from SDMs of other disciplines (e.g., rock mechanics), and 3) a visualization tool for geology, material properties and modeling results. The GSDM is comparable to the GDSMs of SKB and POSIVA in its representation of geology by volume of geologic elements. However, our GSDM is different in that extra information of material properties and an extra tool for visualization is included in the GDSM. The rationale for incorporating material properties and a visualization tool into the GSDM is to expedite the development of the GSDM and SDMs of other disciplines by allowing single institution to integrate database and visualization with the GSDM. SKUA-GOCAD is used for representation of geologic surfaces for ductile and brittle shear zones, and also for surfaces for delineation of volumes of rock units. We have adopted SKUAGOCAD because the program offers powerful functions of interpolation including borehole data and geophysical prospecting. So far, we have tested the program for five different geologies, including sedimentary, high-grade metamorphic, and intrusive igneous geology. The test results are promising. Incorporation of data and modeling results for the SDMs of other disciplines is at conceptual stage. The working conceptual model involves the following steps, 1) to provide the modeler of other disciplines with surface information representing geologic elements, 2) the modeler returns not only material properties but the results of numerical analysis, and 3) incorporation of material properties and modeling results into database. Since the numerical codes in other disciplines adopt different types of formats for 3D geology, we plan to adopt the widely used FEM format prepared by Gmsh. The visualization tool will also adopt Gmsh for graphical representation of 3D geology as well as database for material properties and modeling results. When the working model of GSDM becomes available, rapid and significant progress is expected in the SDMs of other disciplines and related areas, for example, geotechnical investigation for deep geological repository.

The HADES (High-level rAdiowaste Disposal Evaluation Simulator) was developed by the Nuclear Fuel Cycle & Nonproliferation (NFC) laboratory at Seoul National University (SNU), based on the MOOSE Framework developed by the Idaho National Laboratory (INL). As an application of the MOOSE Framework, the HADES incorporates not only basic MOOSE functions, such as multi-physics analysis using Finite Element Method (FEM) and various solvers, but also additional functions for estimating the performance assessment of Deep Geological Repositories (DGR). However, since the MOOSE Framework does not have complex mesh generation and data analyzing capabilities, the HADES has been developed to incorporate these missing functions. In this study, although the Gmsh, finite element mesh generation software, and Paraview, finite element analysis software, were used, other applications can be utilized as well. The objectives of HADES are as follows: (i) assessment of the performance of a Spent Nuclear Fuel (SNF) disposal system concerning Thermal-Hydraulic-Mechanical-Chemical (THMC) aspects; (ii) Evaluation of the integrity of the Engineered Barrier System (EBS) of both general and high-efficiency design perspective; (iii) Collaboration with other researchers to evaluate the disposal system using an open-source approach. To achieve these objectives, performance assessments of the various disposal systems and BMTs (BenchMark Test), conducted as part of the DECOVALEX projects, were studied regarding TH behavior. Additionally, integrity assessments of various DGR systems based on thermal criteria were carried out. According to the results, HADES showed very reasonable results, such as evolutions and distributions of temperature and degree of saturation, when compared to validated code such as TOUGH-FLAC, ROCMAS, and OGS (OpenGeoSys). The calculated data are within the range of estimated results from existed code. Furthermore, the first version of the code, which can estimate the TH behavior, has been prepared to share the contents using Git software, a free and open-source distribution system.

The objective of this study is development of graphite-boron composite material as a replacement for metal canisters to Improve the heat dissipation and radiation shielding performance of dry spent nuclear fuel storage system and reduce the volume of waste storage system. KEARI research team plan to use the graphite matrix manufacturing technology to pelletize the graphite matrix and adjust the content of phenolic resin binder to minimize pore formation. Specifically, we plan to adjust the ratio of natural and synthetic graphite powder and use uniaxial pressing technology to manufacture black graphite matrix with extremely high radial thermal conductivity. After optimizing the thermal conductivity of the graphite matrix, we plan to mix it with selected boron compounds, shape it, and perform sintering and purification heat treatments at high temperatures to manufacture standard composite materials.

Dry storage of nuclear fuel is compromised by threats to the cladding integrity, such as creep and hydride reorientation. To predict these phenomena, spent fuel simulation codes have been developed. In spent fuel simulation, temperature information is the most influential factor for creep and hydride formation. Traditional fuel simulation codes required a user-defined temperature history input which is given by separate thermal analysis. Moreover, geometric changes in nuclear fuel, such as creep, can alter the cask’s internal subchannels, thereby changing the thermal analysis. This necessitates the development of a coupled thermal and nuclear fuel analysis code. In this study, we integrated the 2D FDM nuclear fuel code GIFT developed at SNU with COBRA -SFS. Using this, we analyzed spent nuclear stored in TN-24P dry storage cask over several decades and identified conditions posing threats due to phenomena like creep and hydrogen reorientation, represented by the burnup and peak cladding temperature at the start of dry storage. We also investigated the safety zone of spent nuclear fuel based on burnup and wet storage duration using decay heat.

The development of advanced nuclear facilities is progressing rapidly around the world. Newly designed facilities have differences in structure and operation from existing nuclear facilities, so Safeguards by Design (SBD), which applies safeguards at the design stage, is important. To this end, designers should consider the safeguardability of nuclear facilities when designing the system. Safeguardability represents a measure of the ease of safeguards, and representative evaluation methodologies are Facility Safeguardability Analysis (FSA) and Safeguardability Check-List (SCL). Those two have limitations in the quantification of safeguardability. Accordingly, in this study, the Safeguardability Evaluation Method (SEM), which has clear evaluation criteria based on engineering formulas, was developed. Nuclear Material Accountancy (NMA), a key element of Safeguards, requires the Material Balance Area (MBA) of the target facility and performs Material Balance Evaluation (MBE) based on the quantitative evaluation of nuclear materials entering or leaving the MBA. In this study, about 10 factors related to NMA were developed, including MBA, Key Measurement Point (KMP), Uncertainty of a detector, Radiation signatures, and MUF (Material Unaccounted For). For example, one of the factors, MUF is used in MBA to determine diversion through analysis of unquantified nuclear materials and refers to the difference between Book Inventory and Physical Inventory, as well as errors occurring during the process in bulk facilities, errors in measurement, or intentional use of nuclear materials. This occurs in situations such as attempted diversion, and accurate MUF evaluation is essential for solid Safeguards implementation. MUF can be evaluated using the following formula (MUF=(PB+X-Y)-PE). The IAEA’s Safeguards achievement conditions (MUF < SQ) should be met. Considering this, MUF-related factors were developed as follows. (􀜵􀜧􀜯 = 1 − 􀯆􀯎􀮿 􀯌􀯊 ) In this way, about 10 factors were developed and described in the text. This factors is expected to serve as an important factor in evaluating the safeguardability of NMA, and in the future, safeguardability factors related to Containment & Surveillance (C&S) and Design Information Verification (DIV) will be additionally developed to conduct a comprehensive safeguardability evaluation of the target facility. This methodology can significantly enhance safeguardability during the design stage of nuclear facilities.

The Nuclear Export and Import Control System (NEPS) is currently in operation for nuclear export and import control. To ensure consistent and efficient control, various computational systems are either already in place or being developed. With numerous scattered systems, it becomes crucial to integrate the databases from each to maximize their utility. In order to effectively utilize these scattered computer systems, it is necessary to integrate the databases of each system and develop an associated search system that can be used for integrated databases, so we investigated and analyzed the AI language model that can be applied to the associated search system. Language Models (LM) are primarily divided into two categories: understanding and generative. Understanding Language Models aim to precisely comprehend and analyze the provided text’s meaning. They consider the text’s bidirectional context to understand its deeper implications and are used in tasks such as text classification, sentiment analysis, question answering, and named entity recognition. In contrast, Generative Language Models focus on generating new text based on the given context. They produce new textual content continuously and are beneficial for text generation, machine translation, sentence completion, and storytelling. Given that the primary purpose of our associated search system is to comprehend user sentences or queries accurately, understanding language models are deemed more suitable. Among the understanding language models, we examined BERT and its derivatives, RoBERTa and DeBERTa. BERT (Bidirectional Encoder Representations from Transformers) uses a Bidirectional Transformer Encoder to understand the sentence context and engages in pre-training by predicting ‘MASKED’ segments. RoBERTa (A Robustly Optimized BERT Pre-training Approach) enhances BERT by optimizing its training methods and data processing. Although its core architecture is similar to BERT, it incorporates improvements such as eliminating the NSP (Next Sentence Prediction) task, introducing dynamic masking techniques, and refining training data volume, methodologies, and hyperparameters. DeBERTa (Decoding-enhanced BERT with disentangled attention) introduces a disentangled attention mechanism to the BERT architecture, calculating the relative importance score between word pairs to distribute attention more effectively and improve performance. In analyzing the three models, RoBERTa and DeBERTa demonstrated superior performance compared to BERT. However, considering factors like the acquisition and processing of training data, training time, and associated costs, these superior models may require additional efforts and resources. It’s therefore crucial to select a language model by evaluating the economic implications, objectives, training strategies, performance-assessing datasets, and hardware environments. Additionally, it was noted that by fine-tuning with methods from RoBERTa or DeBERTa based on pre-trained BERT models, the training speed could be significantly improved.