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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.”
John C. Statharas, John G. Bartzis, Demosthenes D. Papailiou
Nuclear Technology | Volume 92 | Number 2 | November 1990 | Pages 248-259
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT90-A34476
Articles are hosted by Taylor and Francis Online.
An improved version of the computer code THEAP-2, suitable for calculation of low flows (G < 50 kg/m2·s−1), is developed. The original code failed to provide reasonable agreement with existing experimental data. The discrepancies were attributed mainly to the drift-flux model, the dispersed flow transition criterion, and the correlations for estimating critical heat flux and minimum film boiling temperatures employed in the original code. The Electric Power Research Institute drift-flux model was used to correct these shortcomings and a new dispersed flow transition criterion was proposed. A review and an assessment of the available correlations of the temperatures resulted in the development of revised versions of these correlations. The changes improved the code’s ability to predict quantities such as the wall and vapor temperatures, the actual quality, and the vapor generation rate. The improvements can be attributed to the transition criterion introduced in the revised code.