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
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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
Nov 2024
Jul 2024
Latest Journal Issues
Nuclear Science and Engineering
December 2024
Nuclear Technology
Fusion Science and Technology
November 2024
Latest News
Nuclear waste: Trying again, with an approach that is flexible and vague
The Department of Energy has started over on the quest for a place to store used fuel. Its new goal, it says, is to foster a national conversation (although this might better be described as many local conversations) about a national problem that can only be solved at the local level with a “consent-based” approach. And while the department is touting the various milestones it has already reached on the way to an interim repository, the program is structured in a way that means its success will not be measurable for years.
Robert W. Carlsen, Paul P. H. Wilson
Nuclear Technology | Volume 195 | Number 3 | September 2016 | Pages 288-300
Technical Paper | doi.org/10.13182/NT15-138
Articles are hosted by Taylor and Francis Online.
Because of the diversity of fuel cycle simulator modeling assumptions, direct comparison and benchmarking can be difficult. In 2012 the Organisation for Economic Co-operation and Development completed a benchmark study that is perhaps the most complete published comparison performed. Despite this, various results from the simulators were often significantly different because of inconsistencies in modeling decisions involving reprocessing strategies, refueling behavior, reactor end-of-life handling, etc. This work identifies and quantifies the effects of selected modeling choices that may sometimes be taken for granted in the fuel cycle simulation domain. Four scenarios are compared using combinations of either fleet-based or individually modeled reactors with either monthly or quarterly (3-month) time steps. The scenarios approximate a transition from the current U.S. once-through light water reactor fleet to a full sodium fast reactor fuel cycle. The Cyclus fuel cycle simulator’s plug-in facility capability along with its market-like dynamic material routing allow it to be used as a level playing field for comparing the scenarios. When they are under supply-constraint pressure, the four cases exhibit noticeably different behavior. Fleet-based modeling is more efficient in supply-constrained environments at the expense of losing insight on issues such as realistically suboptimal fuel distribution and challenges in reactor refueling cycle staggering. Finer-grained time steps also enable more efficient material use in supply-constrained environments resulting in much lower standing inventories of separated Pu. Large simulations with fleet-based reactors run much more quickly than their individual reactor counterparts. Gaining a better understanding of how these and other modeling choices affect fuel cycle dynamics will enable making more deliberate decisions with respect to trade-offs such as computational investment versus realism.