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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 L. Westacott, Craig E. Peterson, Seung Oh
Nuclear Technology | Volume 100 | Number 2 | November 1992 | Pages 228-245
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT92-A34745
Articles are hosted by Taylor and Francis Online.
The RETRAN-03 computer code is validated by simulating two tests that were performed at the Full Integral Simulation Test (FIST) facility. The RETRAN-03 results of a turbine trip (test 4PTT1) and failure to maintain water level at decay power (test T1QUV) are compared with the FIST test data. The RETRAN-03 analysis of test 4PTT1 is compared with a previous TRAC-BWR analysis of the test. Sensitivity to various model nodalizations and RETRAN-03 slip options are studied by comparing results of test T1QUV. The predicted thermal-hydraulic responses of both tests agree well with the test data. The pressure response of test 4PTT1 and the boiloff rate for test T1QUV are accurately predicted. Core uncovery time is found to be sensitive to the upper downcomer and upper plenum nodalization. The RETRAN-03 algebraic and dynamic slip options produce similar results for test T1QUV.
