Abstract
As the demand for electricity surges in the age of artificial intelligence (AI), global tech companies are turning their attention to nuclear microreactors (MRs) as a sustainable energy solution. A groundbreaking remote monitoring technology that can detect potential hazards in these reactors within just two seconds, utilizing advanced AI has been developed. This innovative system promises to monitor the internal conditions of complex nuclear structures in real time, significantly reducing management costs and enhancing safety.
A research team, jointly led by Professor Im Doo Jung, Professor Namhun Kim (Department of Mechanical Engineering), and Professor Hyungmo Kim from Gyeongsang National University has unveiled a smart component system designed for remote surveillance of small nuclear reactors. This advanced system employs embedded optical fiber sensors that continuously analyze smart components, issuing alerts in the event of abnormal conditions.
The key to this breakthrough lies in novel technology that combines 3D printing with AI, enabling the rapid processing of multiple continuous variables from optical fiber sensors. The research team successfully manufactured smart nuclear parts using a Directed Energy Deposition (DED) printing method, seamlessly integrating fiber optic sensors within the metal components. This design ensures stability even in the harsh environments typical of nuclear reactors. The AI system rapidly analyzes data from the optical sensors to monitor thermal deformation in real time, allowing operators to detect irregularities and assess conditions remotely through an augmented reality (AR)-based digital twin interface.
In contrast to traditional large-scale nuclear reactors, microreactors (MRs) offer the ability to provide stable power generation near energy-intensive facilities. However, maintaining safety in these plants is paramount. This new technology is poised to significantly enhance the safety and operational efficiency of next-generation small nuclear reactors by enabling artificial intelligence to monitor critical thermal deformation signals that are often undetectable by human inspection.
Professor Jung noted, "We tackled the challenges associated with traditional inspection methods through our AI convergence technology, which can greatly enhance the stable and efficient operation of next-generation small nuclear power plants." He further predicted that "This convergence technology could extend its applications beyond nuclear power, potentially benefiting diverse industries such as autonomous manufacturing systems, aerospace, and advanced defense."
The findings of this research have been published on October 10 in Virtual and Physical Prototyping, a prestigious academic journal recognized in the top 7 % of Journal Citation Reports (JCR) in advanced manufacturing. The research was supported by the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Korea Institute of Information & Communication Technology Planning & Evaluation (IITP), and the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and the Ministry of Trade, Industry and Energy (MOTIE).
Journal Reference
Hayeol Kim, Junyoung Seo, Adrian Matias Chung Baek, et al., "Direct energy deposition for smart micro reactor," Virtual Phys. Prototyp., (2024).
