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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Hash Hashemian: Visionary leadership
As Dr. Hashem M. “Hash” Hashemian prepares to step into his term as President of the American Nuclear Society, he is clear that he wants to make the most of this unique moment.
A groundswell in public approval of nuclear is finding a home in growing governmental support that is backed by a tailwind of technological innovation. “Now is a good time to be in nuclear,” Hashemian said, as he explained the criticality of this moment and what he hoped to accomplish as president.
Ralph Wiser, Emilio Baglietto
Nuclear Technology | Volume 210 | Number 7 | July 2024 | Pages 1143-1166
Research Article | doi.org/10.1080/00295450.2023.2202802
Articles are hosted by Taylor and Francis Online.
Turbulent heat transfer in buoyancy-dominated flows is a challenging problem for computational fluid dynamics (CFD). Many authors attribute model error in these conditions to the Reynolds analogy. We leverage a brand-new direct numerical simulation database to evaluate the performance of several popular turbulence models in buoyant diabatic channel flow. We find that heat transfer results are relatively accurate, with a Nusselt number error less than 20%. However, the turbulent flow solution is very inaccurate, with wall shear overpredicted by up to 100%. This indicates significant turbulence model error in such flows. We determined that the dominant sources of model error are missing physics in the algebraic Reynolds stress framework and the simple buoyancy production term used in industrial CFD. We suggest that future modeling efforts focus on these two sources of model error. We demonstrate that the Reynolds analogy is not the dominant source of model error.