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
Feb 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
February 2026
Nuclear Technology
Fusion Science and Technology
January 2026
Latest News
From uncertainty to vitality: The future of nuclear energy in Illinois
Nuclear is enjoying a bit of a resurgence. The momentum for reliable energy to support economic development around the country—specifically data centers and AI—remains strong, and strongly in favor of nuclear. And as feature coverage on the states in the January 2026 issue of Nuclear News made abundantly clear, many states now see nuclear as necessary to support rising electricity demand while maintaining a reliable grid and reaching decarbonization goals.
J. Mao, V. Vishwakarma, Z. Welker, C. K. Tai, I. A. Bolotnov, V. Petrov, A. Manera
Nuclear Science and Engineering | Volume 198 | Number 7 | July 2024 | Pages 1404-1425
Research Article | doi.org/10.1080/00295639.2023.2241800
Articles are hosted by Taylor and Francis Online.
To provide computational fluid dynamics (CFD)–grade experimental data for studying stratification, measurements on the High-Resolution Jet (HiRJet) facility at the University of Michigan have been conducted with density differences of and , respectively. Fluid with a density different from the fluid initially present in the HiRJet tank was injected, and the propagation of the time-dependent density stratification was captured on a two-dimensional plane with the aid of the wire-mesh sensor technique for Reynolds numbers near 5000 and Richardson numbers near 0.29. Direct numerical simulations (DNSs) of the two cases have also been conducted to expand the multifidelity database. The novel experimental and DNS data were then used to assess the predictive capabilities of the Standard (SKE) model and the Reynolds Stress Transport (RST) model. In particular, the propagation speed and thickness of the stratification fronts were assessed by comparing the CFD results against the experimental and DNS data. It was found that the general trends of the stratified density fronts were well predicted by the CFD simulations; however, slight overprediction of the thickness of the stratification layer was found with the SKE model while the RST model gave a larger overprediction of the mixing. Examination of the turbulent statistics showed that the turbulent viscosity was largely overpredicted by the RST model compared to the SKE model.