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.
Division Spotlight
Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
Standards Program
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
Aug 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
September 2024
Nuclear Technology
Fusion Science and Technology
August 2024
Latest News
Nuclear new build procurement considerations
It may seem counterintuitive, but the best time to enhance the ability to support operations and maintenance for a new plant is before construction starts. This is one of many lessons learned by the currently operating nuclear fleet. As construction and startup of many nuclear facilities was completed, it quickly became evident that the ability to efficiently support operations and maintenance was limited. Most of the information necessary to establish and manage procurement of spare and replacement items, maintenance, and configuration of the facilities was unavailable and had to be gathered on a case-by-case, “on-demand” basis. Absence of necessary information and the associated challenges resulted in the need for staff augmentation and multiyear-long projects to develop equipment bills of material and maintenance programs and to perform technical evaluations for the huge quantities of spare and replacement items being requested.
Yue Jin, Stephen M. Bajorek, Fan-Bill Cheung
Nuclear Science and Engineering | Volume 197 | Number 5 | May 2023 | Pages 967-986
Technical Paper | doi.org/10.1080/00295639.2022.2087834
Articles are hosted by Taylor and Francis Online.
The accurate prediction of the fluid flow mass and the heat transfer process as well as the system response during reflood transients has long been a critical and challenging issue for reactor system safety analyses. Accurate characterization of the flow and energy transport can also significantly facilitate the various system/component design and optimization tasks. In the current study based on the U.S. Nuclear Regulatory Commission/Pennsylvania State University Rod Bundle Heat Transfer (RBHT) reflood experimental data, a comprehensive uncertainty analysis framework is developed using DAKOTA. The developed framework is used to perform an in-depth reflood model validation and verification for the subchannel analysis code COBRA-TF. In the meantime, the artificial intelligence (AI)–based machine learning (ML) model for rod cladding temperature prediction during reflood is also developed and evaluated using the current framework. Key input parametric effects for reflood thermal-hydraulic prediction include the system pressure, inlet liquid temperature/enthalpy, inlet mass flow rate, and average bundle power input. The figure of merit under consideration is the peak cladding temperature variations. It is found in the current study that, while further model improvement is needed, COBRA-TF can predict the correct parametric trends when compared with the RBHT data. On the other hand, it is challenging for the pure AI-based ML models to correctly reflect the parametric trends. Suggestions for future ML model development are provided in the end.