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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Yunhuang Zhang, Jean C. Ragusa, Jim E. Morel
Nuclear Science and Engineering | Volume 194 | Number 10 | October 2020 | Pages 903-926
Technical Paper | doi.org/10.1080/00295639.2020.1771141
Articles are hosted by Taylor and Francis Online.
The Simplified () approximation is often used to model radiation transport phenomena, but it converges to the true solution of the transport equation only in one-dimensional slab geometry. In all other geometries, it incurs a model error that needs to be quantified. In this paper, we estimate the radiation transport model error due to the approximation and employ transport solutions (with high order) as reference transport solutions. Because the solution does not contain the full angular information of the transport solution, an angular intensity must be reconstructed from the solution in order to compute the model error. We propose two such reconstruction schemes. Model error estimates are given for various quantities of interests, i.e., scalar radiation intensity, radiation flux, and boundary leakage. An adjoint-based approach is proposed to evaluate the model error and is compared against forward and residual techniques. Two-dimensional numerical experiments are presented.