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.
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
Utility Working Conference and Vendor Technology Expo (UWC 2022)
August 7–10, 2022
Marco Island, FL|JW Marriott Marco Island
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
DOE tags Los Alamos to lead $9.25 million molten salt computational research program
The Department of Energy has chosen Los Alamos National Laboratory to lead a $9.25 million collaborative project to model the behavior and properties of structural materials in molten salt through the Scientific Discovery through Advanced Computing (SciDAC) program and announced the news August 9. The team working on the five-year project includes experts from LANL; Carnegie Mellon University; and Idaho, Lawrence Berkeley, and Sandia national laboratories.
Anthony Michael Scopatz
Nuclear Science and Engineering | Volume 186 | Number 1 | April 2017 | Pages 83-97
Technical Paper | dx.doi.org/10.1080/00295639.2016.1272384
Articles are hosted by Taylor and Francis Online.
A method for quickly determining deployment schedules that meet any given fuel cycle demands is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of deployed facilities. Each of these predictions is measured against the demand curve using the dynamic time warping distance. The minimum-distance deployment schedule is evaluated in a full fuel cycle simulation, and the generated production curve then informs the model on the next optimization iteration. The method converges within five to ten iterations to a distance that is less than 1% of the total deployable production. This speed of convergence makes it suitable for use even when fuel cycle realizations are expensive, as in higher-fidelity or agent-based simulators. A representative once-through fuel cycle is used to demonstrate the methodology for reactor deployment. However, the algorithm itself is multivariate and may be used to determine the deployment schedules of many facility types that meet a number of independent criteria simultaneously. The once-through, electricity production example was chosen for the simplicity of illustrating the method.