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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.”
Theo G. Theofanous, Hongfei Yan
Nuclear Technology | Volume 101 | Number 3 | March 1993 | Pages 332-353
Technical Paper | Severe Accident Technology / Nuclear Reactor Safety | doi.org/10.13182/NT93-A34793
Articles are hosted by Taylor and Francis Online.
This is the second part of a three-part series of papers addressing the probability of liner failure in a Mark-I containment. The purpose is to quantify melt release and spreading phenomena in a form suitable for use in the probabilistic framework as discussed in the first part of this series. The quantification of melt release parameters (quantity, superheat, and zirconium content) is derived from an assessment of available system code results and certain independent auxiliary considerations of the physics of the meltdown and slumping processes in the relevant geometries. The quantification of melt spreading phenomena is based primarily on simulant scaled experiments run specifically for this purpose; however, auxiliary considerations of the physics of operative cooling and quenching mechanisms also play a significant role in this assessment.