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Division Spotlight
Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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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Latest News
Oklo to collaborate with Atomic Alchemy on isotope production
Fast reactor developer Oklo, which recently went public on the New York Stock Exchange, announced on May 13 that it has signed a memorandum of understanding with Atomic Alchemy to cooperate on the production of radioisotopes for medical, energy, industry, and science applications.
Bert J. Debusschere, D. Thomas Seidl, Timothy M. Berg, Kyung Won Chang, Rosemary C. Leone, Laura P. Swiler, Paul E. Mariner
Nuclear Technology | Volume 209 | Number 9 | September 2023 | Pages 1295-1318
Research Article | doi.org/10.1080/00295450.2023.2197666
Articles are hosted by Taylor and Francis Online.
Spent nuclear fuel repository simulations are currently not able to incorporate detailed fuel matrix degradation (FMD) process models due to their computational cost, especially when large numbers of waste packages breach. The current paper uses machine learning to develop artificial neural network and k-nearest neighbor regression surrogate models that approximate the detailed FMD process model while being computationally much faster to evaluate. Using fuel cask temperature, dose rate, and the environmental concentrations of CO32−, O2, Fe2+, and H2 as inputs, these surrogates show good agreement with the FMD process model predictions of the UO2 degradation rate for conditions within the range of the training data. A demonstration in a full-scale shale repository reference case simulation shows that the incorporation of the surrogate models captures local and temporal environmental effects on fuel degradation rates while retaining good computational efficiency.