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General Kenneth Nichols and the Manhattan Project
Nichols
The Oak Ridger has published the latest in a series of articles about General Kenneth D. Nichols, the Manhattan Project, and the 1954 Atomic Energy Act. The series has been produced by Nichols’ grandniece Barbara Rogers Scollin and Oak Ridge (Tenn.) city historian David Ray Smith. Gen. Nichols (1907–2000) was the district engineer for the Manhattan Engineer District during the Manhattan Project.
As Smith and Scollin explain, Nichols “had supervision of the research and development connected with, and the design, construction, and operation of, all plants required to produce plutonium-239 and uranium-235, including the construction of the towns of Oak Ridge, Tennessee, and Richland, Washington. The responsibility of his position was massive as he oversaw a workforce of both military and civilian personnel of approximately 125,000; his Oak Ridge office became the center of the wartime atomic energy’s activities.”
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.