Prediction of the operating control rod position of the httr with supervised machine learning

Hai Quan Ho, Satoru Nagasumi, Yosuke Shimazaki, Toshiaki Ishii, Kazuhiko Iigaki, Minoru Goto, Irwan L. Simanullang, Nozomu Fujimoto, Etsuo Ishitsuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution


During operation of the HTTR, hundreds of technical signals and operating conditions must be observed and evaluated to ensure safe operation of the reactor. The accumulated experiment data of the HTTR is not only important for the HTTR operation, but also for the basic development of the HTGR in general. Artificial intelligence (AI) and particularly machine learning (ML) could give the ability to make predictions as well as allow the extraction of key information about physical process from large datasets. Hence, there is a lot of potential to apply AI and ML to predict the operating and safety parameters of the HTTR. In this study, the control rod position of the HTTR is predicted based on ML without using the conventional neutronic codes. The ML with a linear regression algorithm finds a functional relationship between the input dataset and a reference dataset, constructing a function that predicts control rod position from the other operation conditions. As result, the ML gives a good prediction of the HTTR control rod position with less than 5% difference compared to that in the experiment. With increasingly complicated experiments that create a large amount of data, ML is also expected to improve the design and safety analysis of the HTTR in the future.

Original languageEnglish
Title of host publicationNuclear Fuel and Material, Reactor Physics and Transport Theory, and Fuel Cycle Technology
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9784888982566
Publication statusPublished - 2022
Event2022 29th International Conference on Nuclear Engineering, ICONE 2022 - Virtual, Online
Duration: Aug 8 2022Aug 12 2022

Publication series

NameInternational Conference on Nuclear Engineering, Proceedings, ICONE


Conference2022 29th International Conference on Nuclear Engineering, ICONE 2022
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Nuclear Energy and Engineering


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