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Division Spotlight
Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
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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Fusion Science and Technology
Latest News
PPPL study points to better fusion plasma control
The combination of two previously known methods for managing plasma conditions can result in enhanced control of plasma in a fusion reactor, according to a simulation performed by researchers at the Department of Energy’s Princeton Plasma Physics Laboratory.
Juan Jose Ortiz, Ignacio Requena
Nuclear Science and Engineering | Volume 143 | Number 3 | March 2003 | Pages 254-267
Technical Paper | doi.org/10.13182/NSE03-A2334
Articles are hosted by Taylor and Francis Online.
The problem of optimizing refueling in a nuclear boiling water reactor is difficult since it concerns combinatorial optimization and it is NP-Complete. In order to solve this problem, many techniques have been applied, ranging from expert systems to genetic algorithms. In most of these procedures, nuclear reactor simulators are used, which require a longer computation time, to evaluate the goodness of the proposed solutions. As the processes are iterative, many evaluations with the simulator are necessary, and this makes the process extremely slow. In this paper, the use of trained neural networks (NNs) is proposed as an alternative to the simulator, and the results of the NN training are shown in order to predict some variables of interest in the optimization, such as the effective multiplication factor and some thermal limits, related to safety aspects. Finally, a study about the effect of modifying several NN parameters is shown.