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.
Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
2021 Student Conference
April 8–10, 2021
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!
Latest Magazine Issues
Latest Journal Issues
Nuclear Science and Engineering
Fusion Science and Technology
NC State celebrates 70 years of nuclear engineering education
An early picture of the research reactor building on the North Carolina State University campus. The Department of Nuclear Engineering is celebrating the 70th anniversary of its nuclear engineering curriculum in 2020–2021. Photo: North Carolina State University
The Department of Nuclear Engineering at North Carolina State University has spent the 2020–2021 academic year celebrating the 70th anniversary of its becoming the first U.S. university to establish a nuclear engineering curriculum. It started in 1950, when Clifford Beck, then of Oak Ridge, Tenn., obtained support from NC State’s dean of engineering, Harold Lampe, to build the nation’s first university nuclear reactor and, in conjunction, establish an educational curriculum dedicated to nuclear engineering.
The department, host to the 2021 ANS Virtual Student Conference, scheduled for April 8–10, now features 23 tenure/tenure-track faculty and three research faculty members. “What a journey for the first nuclear engineering curriculum in the nation,” said Kostadin Ivanov, professor and department head.
Constantine P. Tzanos
Nuclear Technology | Volume 174 | Number 1 | April 2011 | Pages 41-50
Technical Paper | Heat Transfer | dx.doi.org/10.13182/NT11-A11678
Articles are hosted by Taylor and Francis Online.
In liquid-metal flows, the predictions of the Nusselt number (heat transfer) by Reynolds-averaged Navier-Stokes models of turbulence that use the assumption of a constant turbulent Prandtl number can be significantly off. Heat transfer analyses were performed with a number of turbulence models for flows in a triangular rod bundle and in a pipe, and model predictions were compared with experimental data. Emphasis was placed on the low Reynolds (low-Re) number k- model that resolves the boundary layer and does not use "logarithmic wall functions." The high Reynolds (high-Re) number k- model underpredicts the Nusselt number up to 30%, while the low-Re number model overpredicts it up to 34%. For high Peclet number values, the low-Re number model provides better predictions than the high-Re number model. For Peclet numbers higher than 1500, the predictions of the Reynolds stress model (RSM) are in very good agreement with experimental measurements, but for lower Peclet number values its predictions are significantly off. A relationship was developed that expresses the turbulent Prandtl number as a function of the ratio of the turbulent viscosity to the molecular viscosity. With this modified turbulent Prandtl number, for the flow in the rod bundle the predictions of the low-Re number model are well within the spread of the experimental measurements. For pipe flow, the model predictions are not as sensitive to the correction of the turbulent Prandtl number as they are in the case of the flow in a bundle. The modified low-Re number model underpredicts the limited experimental data by 4%.