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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.”
Yassin A. Hassan, Parvez Salim
Nuclear Technology | Volume 92 | Number 1 | October 1990 | Pages 141-149
Technical Paper | Development of Nuclear Gas Cleaning and Filtering Techniques / Heat Transfer and Fluid Flow | doi.org/10.13182/NT90-A34494
Articles are hosted by Taylor and Francis Online.
A RELAP5 model of a pressurized water reactor (PWR) power plant has been developed. The model of the power plant was a two-loop representation of a PWR power system with U-tube steam generators. A steady-state analysis of the model revealed that RELAP5 underpredicts the heat transfer from the primary to the secondary side of the reactor system. This is due to the fact that RELAP5 uses the heat transfer correlations that were originally developed to calculate the heat transfer coefficients for flow inside tubes, not the tube bundles. In order to mitigate the inaccuracy in the heat transfer predictions, several forms of nucleate boiling and critical heat transfer correlations were employed. As a result, a better heat transfer from the primary to the secondary was achieved. These modifications were also applied to obtain a 0.15-m (6-in.)-diam, cold-leg, small-break loss-of-coolant accident scenario. The response of the transient to these modifications was studied and is presented. The use of the modified correlations produces better steady-state results and predicts plausible transient behavior.