ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Mar 2026
Jan 2026
Latest Journal Issues
Nuclear Science and Engineering
April 2026
Nuclear Technology
February 2026
Fusion Science and Technology
Latest News
Swiss nuclear power and the case for long-term operation
Designed for 40 years but built to last far longer, Switzerland’s nuclear power plants have all entered long-term operation. Yet age alone says little about safety or performance. Through continuous upgrades, strict regulatory oversight, and extensive aging management, the country’s reactors are being prepared for decades of continued operation, in line with international practice.
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.