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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.
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2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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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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New laws offer nuclear industry incentives for existing power plant uprates
This year, the U.S. nuclear industry received a much-needed economic boost that could help preserve operating nuclear power plants and incentivize upgrades that extend their lifespan and power output.
Signed into law in 2022, the Inflation Reduction Act offers production tax credits (PTCs) for existing nuclear power plants and either PTCs or investment tax credits (ITCs) for new carbon-free generation. These credits could make power uprates—increasing the maximum power level at which a commercial plant may operate—a much more appealing option for utilities.
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.