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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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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Latest News
College students help develop waste-measuring device at Hanford
A partnership between Washington River Protection Solutions (WRPS) and Washington State University has resulted in the development of a device to measure radioactive and chemical tank waste at the Hanford Site. WRPS is the contractor at Hanford for the Department of Energy’s Office of Environmental Management.
Juan José Ortiz, Alejandro Castillo, José Luis Montes, Raúl Perusquía, José Luis Hernández
Nuclear Science and Engineering | Volume 162 | Number 2 | June 2009 | Pages 148-157
Technical Paper | doi.org/10.13182/NSE162-148
Articles are hosted by Taylor and Francis Online.
RENO-CC, a system to optimize nuclear fuel lattices for boiling water reactors using a multistate recurrent neural network, is shown. This kind of neural network is formed by only one layer of neurons. Each neuron is associated with a pin of the fuel lattice array. RENO-CC was tested through the fuel lattice design of 10 × 10 arrays with two water channels. Thus, the neural network has a total of 51 neurons; four neurons are associated with the channels (they correspond to a half fuel lattice). The neuron's outputs are known as the neural states. The RENO-CC's neural network works by changing the neural states in order to decrease or increase the value of an objective function. Neural states are chosen from an inventory of pins with different 235U enrichment and gadolinia concentrations. The objective function includes both the local power peaking factor and the infinite multiplication factor. These parameters are calculated with the HELIOS code. A fuzzy logic system is applied in order to decide if the designed fuel lattice is suitable to be evaluated by a three-dimensional reactor core simulator. To carry out the assessment, the fuel lattices with the best fuzzy qualification are placed at the bottom zone of a predesigned fuel assembly and predesigned fuel loading and control rod patterns. Fuel lattice performance is verified with the Core Master PRESTO core simulator. According to the obtained results, RENO-CC could be considered as a powerful tool to design fuel lattices. The system was programmed with Fortran 77 using a UNIX interface in an Alpha workstation.