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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.”
Michael D. Allen, Harlan W. Stockman, Kenneth O. Reil, Arthur J. Grimley
Nuclear Technology | Volume 92 | Number 2 | November 1990 | Pages 214-228
Technical Paper | Nuclear Fuel | doi.org/10.13182/NT90-A34472
Articles are hosted by Taylor and Francis Online.
High-burnup uranium dioxide reactor fuel was heated in-pile at ∼2490 K in a reducing atmosphere (33% H2 in argon) for 16 min. Fission product aerosols and vapors released from the fuel were collected on a series of sequentially opened filters; the fractions of the original fuel inventory collected on the filters were f Cs = 0.56, f I = 0.38, f Ba = 0.078, f Sr = 0.053, f Eu = 0.064, and f Te < 0.002. The measured release rates for nonvolatile fission products were much higher than predicted by existing release codes, whereas tellurium release was much lower. Posttest examination of the fuel indicates extensive fuel/clad interaction, fuel swelling, and infiltration of the fuel by a zirconium-rich metallic melt; this melt kept oxygen potentials in the fuel very low. The low oxygen potentials and fuel disruption may account for the discrepancy between release codes and the test release results.