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Division Spotlight
Nuclear Nonproliferation Policy
The mission of the Nuclear Nonproliferation Policy Division (NNPD) is to promote the peaceful use of nuclear technology while simultaneously preventing the diversion and misuse of nuclear material and technology through appropriate safeguards and security, and promotion of nuclear nonproliferation policies. To achieve this mission, the objectives of the NNPD are to: Promote policy that discourages the proliferation of nuclear technology and material to inappropriate entities. Provide information to ANS members, the technical community at large, opinion leaders, and decision makers to improve their understanding of nuclear nonproliferation issues. Become a recognized technical resource on nuclear nonproliferation, safeguards, and security issues. Serve as the integration and coordination body for nuclear nonproliferation activities for the ANS. Work cooperatively with other ANS divisions to achieve these objective nonproliferation policies.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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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Latest News
Dragonfly, a Pu-fueled drone heading to Titan, gets key NASA approval
Curiosity landed on Mars sporting a radioisotope thermoelectric generator (RTG) in 2012, and a second NASA rover, Perseverance, landed in 2021. Both are still rolling across the red planet in the name of science. Another exploratory craft with a similar plutonium-238–fueled RTG but a very different mission—to fly between multiple test sites on Titan, Saturn’s largest moon—recently got one step closer to deployment.
On April 25, NASA and the Johns Hopkins University Applied Physics Laboratory (APL) announced that the Dragonfly mission to Saturn’s icy moon passed its critical design review. “Passing this mission milestone means that Dragonfly’s mission design, fabrication, integration, and test plans are all approved, and the mission can now turn its attention to the construction of the spacecraft itself,” according to NASA.
Atul A. Karve, Paul J. Turinsky
Nuclear Technology | Volume 131 | Number 1 | July 2000 | Pages 48-68
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT00-A3104
Articles are hosted by Taylor and Francis Online.
As part of the continuing development of the boiling water reactor in-core fuel management optimization code FORMOSA-B, the fidelity of the core simulator has been improved and a control rod pattern (CRP) sampling capability has been added. The robustness of the core simulator is first demonstrated by benchmarking against core load-follow depletion predictions of both SIMULATE-3 and MICROBURN-B2 codes. The CRP sampling capability, based on heuristic rules, is next successfully tested on a fixed fuel loading pattern (LP) to yield a feasible CRP that removes the thermal margin and critical flow constraint violations. Its performance in facilitating a spectral shift flow operation is also demonstrated, and then its significant influence on the cost of thermal margin is presented. Finally, the heuristic CRP sampling capability is coupled with the stochastic LP optimization capability in FORMOSA-B - based on simulated annealing (SA) - to solve the combined CRP-LP optimization problem. Effectiveness of the sampling in improving the efficiency of the SA adaptive algorithm is shown by comparing the results to those obtained with the sampling turned off (i.e., only LP optimization is carried out for the fixed reference CRP). The results presented clearly indicate the successful implementation of the CRP sampling algorithm and demonstrate FORMOSA-B's enhanced optimization features, which facilitate the code's usage for broader optimization studies.
