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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Latest News
Atomic Canyon partners with INL on AI benchmarks
As interest and investment grows around AI applications in nuclear power plants, there remains a gap in standardized benchmarks that can quantitatively compare and measure the quality and reliability of new products.
Nuclear-tailored AI developer Atomic Canyon is moving to fill that gap by entering into a new strategic partnership with Idaho National Laboratory to develop and release the “first comprehensive benchmark suite for evaluating retrieval-augmented generation (RAG) and large language models (LLMs) in nuclear applications.”
Masao Hamada, Kunio Uehara, Kazuyoshi Muramatsu, Takanobu Kamei, Tetsuo Tamaoki, Mitsuaki Yamaoka, † Yukio Sonoda, Yuji Sano, Masuo Sato, Takayuki Sudo
Nuclear Technology | Volume 98 | Number 1 | April 1992 | Pages 1-13
Technical Paper | Fast Reactor Safety / Fission Reactor | doi.org/10.13182/NT92-A34646
Articles are hosted by Taylor and Francis Online.
Detection and location of failed fuel in a liquidmetal fast breeder reactor (LMFBR) are very important both for safety and availability. When a fuel failure is detected, it is desirable to identify the failed subassembly quickly to reduce plant shutdown time. The flux tilting method is expected to effectively identify the defective subassembly. The feasibility of the flux tilting method is investigated for an LMFBR with a 1000-MW(electric) homogeneous core. A numerical simulation is performed to estimate the viability of the flux tilting method, and a combination of the flux tilting method and the sipping method is found to be very effective in identifying the failed subassembly. A functional scheme for a computer-aided failed fuel detection and location system is discussed as part of a future online support system.
