ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Division Spotlight
Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
June 2024
Nuclear Technology
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Latest News
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Carlos X. Soto, Odera Dim, Yonggang Cui, Warren Stern
Nuclear Technology | Volume 209 | Number 9 | September 2023 | Pages 1282-1294
Research Article | doi.org/10.1080/00295450.2023.2200573
Articles are hosted by Taylor and Francis Online.
Burnup measurement is an important step in material control and accountancy at nuclear reactors and may be done by examining gamma spectra of fuel samples. Traditional approaches rely on known correlations to specific photopeaks (e.g., Cs) and operate via a standard linear regression method. However, the quality of these regression methods is limited even in the best case and is significantly poorer at short fuel cooldown times, due to the elevated radiation background by short-lifetime isotopes and self-shielding effect of the fuel. For practical operation of pebble bed reactors (PBRs), quick measurements (in minutes) and short cooling times (in hours) are required from a safety and security perspective. We investigated the efficacy and performance of machine learning (ML) methods to predict the burnup of the pebble fuel from full gamma spectra (rather than specific discrete photopeaks) and found a full-spectrum ML approach to far outperform baseline regression predictions in all measurement and cooling conditions, including in operational-like measurement conditions. We also performed model and data ablation experiments to determine the relative performance impact of our ML methods’ capacity to model data nonlinearities and the inherent additional information in full spectra. Applying our ML methods, we found a number of surprising results, including improved accuracy at shorter fuel cooling times (the opposite of the norm), remarkable robustness to spectrum compression (via rebinning), and competitive burnup predictions even when using a background signal only (i.e., explicitly omitting known isotope photopeaks).