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Nuclear Science and Engineering
Fusion Science and Technology
Baranwal reviews virtual STEM lessons for U.S. tribal communities
In a blog post to the Department of Energy’s website on November 23, Rita Baranwal, assistant secretary for the Office of Nuclear Energy, commended recent virtual lesson projects from the Office of Nuclear Energy and the Nuclear Energy Tribal Working Group to increase STEM opportunities for Native American tribes.
The spotlighted lesson discussed in the article focused on a 3D-printed clip that turns a smartphone or tablet into a microscope with the ability to magnify items by 100 times. The Office of Nuclear Energy shipped nearly 1,000 of these microscope clips to students across the country, many of them going to U.S. tribal communities.
David J. Kropaczek, Ryan Walden
Nuclear Science and Engineering | Volume 193 | Number 5 | May 2019 | Pages 523-536
Technical Paper | dx.doi.org/10.1080/00295639.2018.1550970
Articles are hosted by Taylor and Francis Online.
The constraint annealing method is presented and demonstrated for the solution of large-scale, multiconstrained problems in light water reactor fuel cycle optimization. Constraint annealing is a penalty-free method that eliminates the need for traditional constraint weighting factors by treating each objective function and constraint as separate and concurrently solved minimization problems within a global optimization search framework. The current application seeks to demonstrate the effectiveness of constraint annealing for a complex core loading pattern design problem containing multiple objective functions and constraints without the need for additional ad hoc control parameters. Two problems of varying degrees of complexity are analyzed. The first problem is defined by a single objective function based on maximizing cycle energy with two constraints based on power peaking and peak rod exposure. The second problem expands upon the first by adding an additional objective function for vessel fluence and four additional constraints based on controlled power peaking, steaming rate, moderator temperature coefficient, and alternate source term. Results demonstrate that constraint annealing inherently addresses issues of scaling associated with different objective function and constraint formulations as well as the impact on cycle energy.