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Division Spotlight
Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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
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Fusion Science and Technology
Latest News
Uncertainty contributes to lowest uranium spot prices in 18 months
A combination of plentiful supply and uncertain demand resulted in spot pricing for uranium closing out March below $64 per pound, with dips down to about $63.50 during mid-March—the lowest futures prices in 18 months, according to tracking by analysis firm Trading Economics. Spot prices have also fallen steadily since the beginning of 2024. Meanwhile, long-term prices have held steady at about $80 per pound at the end of March, according to Canadian front-end uranium mining, milling, and conversion company Cameco.
Huajiang Jin, Shuaishuai Zhang, Jianxiang Zheng, Jian Zhang, Huifang Miao, Liuxuan Cao
Fusion Science and Technology | Volume 80 | Number 5 | July 2024 | Pages 682-694
Research Article | doi.org/10.1080/15361055.2023.2232229
Articles are hosted by Taylor and Francis Online.
Understanding irradiation-induced degradation processes of nuclear structural materials is essential for creating methodologies and procedures for nuclear reactor safety. Due to the time- and resource-intensive property of both experiments and multiscale simulations of irradiation damage, the trial-and-error approach is completely inefficient. Recently, machine learning techniques have been employed to predict the properties of reduced activation ferritic martensitic (RAFM) steels, such as yield strength and elongation, as well as irradiation embrittlement in steel pressure vessels, with encouraging progress.
In this work, void swelling is predicted using a machine learning method for the first time, taking into account the synergistic effects of displacement damage, helium, and hydrogen. Assisted by the analysis of feature engineering, seven machine learning models are trained and compared by multicriteria evaluation methods. Finally, the parameter-optimized gradient-boosting model is selected as the mapping function with the highest accuracy and universality to predict void swelling. In particular, the dependence of the void swelling and the injection amount of helium and hydrogen in the continuous parameter variation range is predicted beyond the existing experimental data. This work demonstrates the feasibility of machine learning to predict material irradiation damage by synergistic effects and has practical significance in nuclear material optimization and reactor safety.