Preliminary study of artificial intelligence-based fuel-rod pattern analysis of low-quality tomographic image of fuel assembly

Saerom Seong, Sei Hwan Choi, Jae Joon Ahn, Hyung joo Choi, Yong Hyun Chung, Sei Hwan You, Yeon Soo Yeom, Hyun Joon Choi, Chul Hee Min

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalNuclear Engineering and Technology
Publication statusPublished - 2022 Oct

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2022 Korean Nuclear Society

All Science Journal Classification (ASJC) codes

  • Nuclear Energy and Engineering


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