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Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
2021 Student Conference
April 8–10, 2021
North Carolina State University|Raleigh Marriott City Center
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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ANS Board of Directors votes to retire outdated position statements
The American Nuclear Society’s Board of Directors on November 19 voted to retire several outdated position statements, as requested by the Public Policy Committee. Among them are Position Statements #37 and #63, dating from 2010, which have been retired for lacking policy recommendations and for being redundant, as other position statements exist with language that better articulates the Society’s stance on those topics.
David J. Kropaczek, Ryan Walden
Nuclear Science and Engineering | Volume 193 | Number 5 | May 2019 | Pages 506-522
Technical Paper | dx.doi.org/10.1080/00295639.2018.1554173
Articles are hosted by Taylor and Francis Online.
A method is developed, assessed, and demonstrated for addressing objective functions and constraints within the context of combinatorial optimization problems. The penalty-free method developed, referred to as constraint annealing, eliminates the use of traditional constraint penalty factors by treating the objective functions and constraints as separate and concurrently solved minimization problems within a global optimization search framework. The basis of the constraint annealing algorithm is a highly scalable method based on the method of parallel simulated annealing with mixing of states. Unique to constraint annealing is a novel approach that employs both global solution acceptance and local objective function and constraint statistics in the calculation of adaptive cooling temperatures that are specific to each objective function and constraint. The constraint annealing method is assessed against a traditional penalty-factor approach for a realistic core loading pattern design problem and shown to be robust with respect to elimination of arbitrary weighting factors on constraint values. In addition, the constraint annealing method is demonstrated to be robust with respect to parallel scaling as well as improved optimization performance on high-performance-computing systems.