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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.”
Ki-Yong Ra, Byong-Whi Lee, Soon-Heung Chang
Nuclear Technology | Volume 101 | Number 2 | February 1993 | Pages 149-158
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT93-A34776
Articles are hosted by Taylor and Francis Online.
With most of the current probabilistic safety criteria (PSCs), it is difficult to select a unanimously acceptable single-point safety goal and to evaluate the uncertainties in the results of a probabilistic safety assessment (PSA). A new PSC is proposed, in which the distribution of the public’s safety goals (DPSG) is used as a benchmark for evaluating the results of a PSA rather than a single-point safety goal. With this approach, the DPSG and the uncertainties in the results of a PSA can be handled properly so as to give a clear answer of “how much of the public feels a nuclear reactor is safe” to the question of “How safe is safe enough?” The proposed PSC is compared with the current PSCs and the expected utility model. If the actual DPSG is unavailable or difficult to obtain directly, a lognormal distribution is recommended as an appropriate DPSG for core melt frequencies in terms of maximizing entropy and minimizing total social cost. The proposed DPSG and PSC for core melt frequency are applied to the results of NUREG-1150.