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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
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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June 2024
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May 2024
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Latest News
Commercial nuclear innovation "new space" age
In early 2006, a start-up company launched a small rocket from a tiny island in the Pacific. It exploded, showering the island with debris. A year later, a second launch attempt sent a rocket to space but failed to make orbit, burning up in the atmosphere. Another year brought a third attempt—and a third failure. The following month, in September 2008, the company used the last of its funds to launch a fourth rocket. It reached orbit, making history as the first privately funded liquid-fueled rocket to do so.
David J. Kropaczek, Ryan Walden
Nuclear Science and Engineering | Volume 193 | Number 5 | May 2019 | Pages 506-522
Technical Paper | 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.