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Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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Latest News
Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
M. Marseguerra, F. Mazzarella
Nuclear Science and Engineering | Volume 133 | Number 3 | November 1999 | Pages 293-300
Technical Paper | doi.org/10.13182/NSE99-A2089
Articles are hosted by Taylor and Francis Online.
Nowadays, using artificial neural networks (ANNs) to perform interesting input/output mappings in various industrial contexts has become almost routine. Indeed, the nonlinear features of this algorithm allow one to deal with real complex systems such as those encountered in the nuclear field.Here, an ANN algorithm is applied to determine the relationships that exist between some process variables pertaining to the operation of the steam generator of a pressurized water reactor. The exemplars required for the ANN training are obtained from a suitable nonlinear, mathematical model, numerically integrated, whose solution yields pseudo-experimental data that simulate data that would be collected in a real experiment. In the training phase, Ishikawa structural learning that aims at eliminating the unnecessary network connections is performed. After completion of training, without the analyst's intervention, the resulting ANN topology consists of the superposition of three distinct and smaller ANNs. This implies that the network, on the basis of the given exemplars only, without knowledge of the physical laws, is able by itself to decide that the relevant input/output variables could be partitioned in independent groups. The ANNs so identified turn out to be so simple that their mappings could be easily translated into empirical algebraic correlations. Numerical tests validate the correlations thereby obtained.