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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.”
Jeffrey T. Dillingham, James H. Stuhmiller
Nuclear Technology | Volume 100 | Number 2 | November 1992 | Pages 260-270
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT92-A34747
Articles are hosted by Taylor and Francis Online.
Critical heat flux (CHF) in boiling water and pressurized water reactors is investigated using a three-pronged approach. First, a physically realistic and mathematically rigorous computational model is developed to describe and simulate the transitions between flow regimes. This is called the dynamic flow regime model (DFRM). Second, extensive reanalysis of the Columbia University CHF experimental data is performed to shed light on the processes at work. This analysis indicates that the mechanism for wall drying may not follow conventional wisdom. The DFRM has therefore been supplemented with a semiempirical liquid entrainment model, which accounts for the dynamics of bubble formation. The model produces CHF predictions that agree with the Columbia data slightly better than the Columbia correlation function. Third, to develop a mechanistic understanding of the empirical model, detailed microscale simulations of boiling are performed using the EITACC computer code. EITACC solves the Navier-Stokes equations for three-dimensional two-phase flow using a finite difference method. EITACC has been used to produce time-lapse images of bubble formation at a wall during subcooled boiling. These images provide insight into the mechanisms of bubble separation from the wall, bubble collapse due to condensation, wall drying, and liquid entrainment. This insight is used to improve and validate the DFRM.