This study aims to classify R&D activities related to the nuclear fuel cycle using the deep learning methodology. First, R&D data of the Republic of Korea were collected from the National Science & Technology Information Service (NTIS) for the years 2021, 2022, and 2023. We use keywords such as ‘nuclear,’ ‘uranium,’ ‘plutonium,’ and ‘thorium’ to find nuclear-related R&D projects in the NTIS database. Among the numerous R&D projects found through keyword searches, overlapping and medical-related R&D projects were excluded. Finally, 495 R&D projects conducted in 2021, 430 R&D projects conducted in 2022, and 296 R&D projects conducted in 2023 were obtained for analysis. After that, Safeguards experts determine whether the R&D projects are subject to declaration under the AP. The values of the content validity index (CVI) and content validity ratio (CVR) were used to verify whether the experts’ judgments were valid. The 1,218 collected and labeled data were then divided 8:2 into training and test datasets to see if deep learning could be applied to classify nuclear fuel cycle-related R&D activities. We use the Python and TensorFlow packages, including RNN, GRU, and CNN methods. First, the collected text information was preprocessed to remove punctuation marks and then tokenized to make it suitable for deep learning. After 20 epochs of training to classify the nuclear fuel cycle-related R&D activities, the RNN model achieved 97.30% accuracy and a 5.85% error rate on the validation dataset. The GRU model achieved 96.53% accuracy and a 9.06% error rate on the validation dataset. In comparison, the CNN model achieved 94.61% accuracy and a 2.57% error rate on the validation dataset. When applying the test dataset to each model, the RNN model had a test accuracy of 83.20%, the GRU test accuracy of 82.79%, and the CNN model had a test accuracy of 85.66% for the same dataset. This study applied deep learning models to labeled data judged by various experts, and the CNN model showed the best results. In the future, this study will continue to develop an optimum deep learning model that can classify nuclear fuel cycle-related R&D activities to achieve the purpose of safeguards measures from open-source data such as papers and articles.

The Radiation and Decommissioning Laboratory of Central Research Institute (CRI) of Korea Hydro and Nuclear Power Co. (KHNP) performs research to technically support the effective management of radiological hazards to avoid risks to civilians, the workers, and the environment from the radiological risks. The laboratory mainly consists of three technical groups: decommissioning and SF technology group, radiation and chemistry group, and radwaste and environment group. The groups carry out various R&D such as decommissioning, spent fuel management, radiation protection, water chemistry management, and radioactive waste management. The laboratory also technically supports the calibration of radiometric instruments as a Korea Laboratory Accreditation Scheme (KOLAS), approval for decommissioning, guidance for radioactive waste management, state-of-the-art technology evaluations, and technology transfer.

Sandia National Laboratories is the lead laboratory for the United States Department of Energy for the research and development (R&D) efforts to support the technical basis for the long-term storage, subsequent transportation, and permanent disposal of commercial spent nuclear fuel and high-level waste. Sandia does not design nuclear facilities; Sandia performs R&D to help ensure facilities and the fuel cycle are safe, sustainable, and secure. This talk will focus on the spent fuel storage and transportation programs that contribute to this work. The goal in spent fuel storage and transportation R&D is to understand the mechanical integrity of the fuel, cladding, and storage system beyond interim storage and into disposal time frames. Our research is focused on understanding the high burn-up cladding integrity over time, understanding the thermal behavior during drying and storage, understanding potential cladding oxidation pathways, and quantifying in the external loads experienced during transportation, handling, and seismic events. Additionally, this work includes extensive work to understand the basic science of canister stress corrosion cracking and the potential consequences of a through wall canister crack.

Traditionally Nuclear Research and Development (R&D) result has been big influence on other industries and societies and it requires large scale investments and study period. So it is essential to apply Quality Assurance (QA) for systematic R&D management

A institute developed Quality Assurance(QA) program for nuclear R&D projects to meet the demands of its customers' requirements for recognized quality standards and nuclear industry accepted practices. It was implemented by project quality assurance plan as a new process. This paper is designed to introduce the process of establishment and execution of nuclear quality assurance programs for R&D as a case study. This QA program can be used as a reference to other organization on implementation of QA for R&D projects.