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Division Spotlight
Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
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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Jul 2024
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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.
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.