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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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.”
Nobuyuki Nonaka, Ikken Sato
Nuclear Technology | Volume 98 | Number 1 | April 1992 | Pages 54-69
Technical Paper | Fast Reactor Safety / Nuclear Reactor Safety | doi.org/10.13182/NT92-A34650
Articles are hosted by Taylor and Francis Online.
An improved method to evaluate key phenomena in the initiating-phase energetics of unprotected lossof-flow (ULOF) whole-core accidents in liquid-metal fast breeder reactors is presented. Three phenomena, namely, axial fuel expansion, fuel failure, and postfailure fuel motion, have been examined through the CABRI-1 in-pile experiments and analyses with special emphasis on the self-limiting mechanisms of the energetics potential. For the experiment analyses, the SAS3D, PAPAS-2S, and SAS4A computer codes are employed selectively to obtain a detailed investigation of the phenomena and to validate physical models. The improved knowledge obtained through the research efforts in CABRI-1 and relevant safety experiments has been implemented in the revised SAS3D code. This evaluation method, which accounts for the self-limiting mechanisms, has been applied to a reactor analysis of an energetic ULOF sequence. The results of the application study confirm the importance and effectiveness of the method.