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
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
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!
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Nuclear Science and Engineering
August 2024
Nuclear Technology
Fusion Science and Technology
Latest News
ARPA-E announces $40 million to develop transmutation technologies for UNF
The Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) announced $40 million in funding to develop cutting-edge technologies to enable the transmutation of used nuclear fuel into less-radioactive substances. According to ARPA-E, the new initiative addresses one of the agency’s core goals as outlined by Congress: to provide transformative solutions to improve the management, cleanup, and disposal of radioactive waste and spent nuclear fuel.
Anthony Michael Scopatz
Nuclear Technology | Volume 195 | Number 3 | September 2016 | Pages 273-287
Technical Paper | doi.org/10.13182/NT15-153
Articles are hosted by Taylor and Francis Online.
This paper presents a new fuel cycle benchmarking analysis methodology by coupling Gaussian process (GP) regression, a popular technique in machine learning, to dynamic time warping, a mechanism widely used in speech recognition. Together, they generate figures of merit (FOMs) for a suite of fuel cycle realizations. The FOMs may be computed for any time series metric that is of interest to a benchmark. For a given metric, these FOMs have the advantage that they reduce the dimensionality to a scalar and are thus directly comparable. The FOMs account for uncertainty in the metric itself, utilize information across the whole time domain, and do not require that the simulators use a common time grid. Here, a distance measure is defined that can be used to compare the performance of each simulator for a given metric. Additionally, a contribution measure is derived from the distance measure that can be used to rank order the impact of different partitions of a fuel cycle metric. Lastly, this paper warns against using standard signal-processing techniques for error reduction, as error reduction is better handled by the GP regression itself.