Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Preliminary study of artificial intelligence-based fuel-rod pattern analysis of low-quality tomographic image of fuel assembly

  • Seong, Saerom (Department of Radiation Convergence Engineering, Yonsei University) ;
  • Choi, Sehwan (Department of Artificial Intelligence, Hanyang University) ;
  • Ahn, Jae Joon (Division of Data Science, Yonsei University) ;
  • Choi, Hyung-joo (Department of Radiation Convergence Engineering, Yonsei University) ;
  • Chung, Yong Hyun (Department of Radiation Convergence Engineering, Yonsei University) ;
  • You, Sei Hwan (Department of Radiation Oncology, Yonsei University Wonju College of Medicine) ;
  • Yeom, Yeon Soo (Department of Radiation Convergence Engineering, Yonsei University) ;
  • Choi, Hyun Joon (Department of Radiation Oncology, Yonsei University Wonju College of Medicine) ;
  • Min, Chul Hee (Department of Radiation Convergence Engineering, Yonsei University)
  • Received : 2022.01.08
  • Accepted : 2022.05.13
  • Published : 2022.10.25
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Abstract

Single-photon emission computed tomography is one of the reliable pin-by-pin verification techniques for spent-fuel assemblies. One of the challenges with this technique is to increase the total fuel assembly verification speed while maintaining high verification accuracy. The aim of the present study, therefore, was to develop an artificial intelligence (AI) algorithm-based tomographic image analysis technique for partial-defect verification of fuel assemblies. With the Monte Carlo (MC) simulation technique, a tomographic image dataset consisting of 511 fuel-rod patterns of a 3 × 3 fuel assembly was generated, and with these images, the VGG16, GoogLeNet, and ResNet models were trained. According to an evaluation of these models for different training dataset sizes, the ResNet model showed 100% pattern estimation accuracy. And, based on the different tomographic image qualities, all of the models showed almost 100% pattern estimation accuracy, even for low-quality images with unrecognizable fuel patterns. This study verified that an AI model can be effectively employed for accurate and fast partial-defect verification of fuel assemblies.

Keywords

Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A1A01059875), the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety (KoFONS) using financial resources granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 2101073), the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. G032579811).

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