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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.
L. T. Fan, David F. Aldis
Nuclear Technology | Volume 32 | Number 3 | March 1977 | Pages 222-238
Technical Paper | Reactor | doi.org/10.13182/NT77-A31747
Articles are hosted by Taylor and Francis Online.
The design of a cooling system for a power plant is approached as a problem in systems synthesis. The cooling system is optimized with respect to both the independent variables associated with each cooling unit and the arrangement of the cooling units in the system. A sample cooling system is optimally synthesized with two methods of systems synthesis—a structural parameter problem formulation with a direct search and a dynamic programming formulation. Other approaches are also considered. Two types of cooling units—a mechanical draft cooling tower and a cooling pond—are used in the example. The methods proposed for the development of the optimal design are sufficiently general so that any number, type, or combinations of types of cooling units could be included in the optimal systems synthesis.