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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.”
Y. W. Wang, B. S. Pei, W. K. Lin
Nuclear Technology | Volume 95 | Number 1 | July 1991 | Pages 87-94
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT91-A34570
Articles are hosted by Taylor and Francis Online.
Methods using the signals detected by a single void fraction sensor to identify four kinds of typical vertical, cocurrent, upward, two-phase tube flow patterns are investigated. By analyzing 100 sets of time-varying void fraction signals acquired from an impedance device in an air-water two-phase loop, the results of the various methods are evaluated and demonstrated. With the high-frequency contribution fraction (HFCF) criteria, the success rate is 81%. An auxiliary criterion (the void fraction criterion) is proposed to increase the success rate to 92%. The results and the criteria from this study are compared with earlier studies. From the comparison, the applicability of the HFCF criterion to a system in which void fraction can be measured directly is verified.