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 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
Latest Magazine Issues
Jul 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
September 2026
Nuclear Technology
August 2026
Fusion Science and Technology
Latest News
The human factor in licensing and operating the next generation of nuclear plants
As human factors specialists working at the intersection of human performance and nuclear operations, we are witnessing one of the nuclear sector’s most significant transitions in decades. The emergence of small modular reactors, microreactors, and other advanced designs is reshaping the industry’s landscape. Digital instrumentation and controls, passive safety systems, and increased automation are creating opportunities for greater safety margins and more flexible operation. These same features also fundamentally redefine what it means to “operate” a nuclear plant. Interactions among human roles, automation, and passive systems shape how people maintain awareness, exercise judgment, and intervene when necessary. These developments affect both operational realities and the regulatory foundations on which nuclear safety is built.
Constantine P. Tzanos
Nuclear Technology | Volume 183 | Number 1 | July 2013 | Pages 88-100
Technical Paper | Thermal Hydraulics | doi.org/10.13182/NT13-A16994
Articles are hosted by Taylor and Francis Online.
Heat transfer coefficients have been computed for flow in a pipe and flow between two plates with correlations and turbulence models based on Reynolds Averaging of the Navier-Stokes (RANS) equations. Predictions of the correlations and those of RANS turbulence models have been compared with experimental data of flow in a pipe. The correlations considered are those of Dittus-Boelter, Seider-Tate, Petukhov, and Sleicher-Rouse, while the turbulence models include the standard high Reynolds number, the Reynolds stress model, the low Reynolds number, and the v2f model. There are significant differences in the predictions of the correlations as well as in those of the turbulence models. Although computational fluid dynamics simulations have wider applicability and provide more information than simulations using correlations, the heat transfer coefficient predicted by the turbulence models is not always more accurate than that predicted by correlations. The discrepancy in the heat transfer coefficient predicted by the turbulence models is due mainly to discrepancies in the prediction of turbulence near the wall and to the uncertainty in the value of the turbulent Prandtl number.