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
60 Years of U: Perspectives on resources, demand, and the evolving role of nuclear energy
Recent years have seen growing global interest in nuclear energy and rising confidence in the sector. For the first time since the early 2000s, there is renewed optimism about the industry’s future. This change is driven by several major factors: geopolitical developments that highlight the need for secure energy supplies, a stronger focus on resilient energy systems, national commitments to decarbonization, and rising demand for clean and reliable electricity.
Kuan-Chywan Tu, Chien-Hsiung Lee, Shih-Jen Wang, Bau-Shei Pei
Nuclear Technology | Volume 124 | Number 3 | December 1998 | Pages 243-254
Technical Paper | Thermal Hydraulics | doi.org/10.13182/NT98-A2923
Articles are hosted by Taylor and Francis Online.
A new mechanistic critical heat flux (CHF) model has been developed for flow boiling CHF data of low-pressure (i.e., 0.2 to 4.0 MPa), low-mass-flux (i.e., 189 to 789 kg/m2s), and high-quality conditions. In general, CHF at these conditions associates with the flow regime of annular flow. This model assumes that the Helmholtz instability at the liquid-vapor interface of annular flow triggers the onset of CHF. CHF is the energy required to dryout the liquid film isolated by flow instability. With five empirical constants to properly correlate the liquid-vapor configurations of annular flow in the steam-water systems, the model successfully achieves a mean deviation error of 10.2% over a CHF data set consisting of 733 CHF data. The prediction of this model is more accurate than those of Biasi and Bowring correlations at the aforementioned low-pressure and low-mass-flux conditions.