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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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Fusion Science and Technology
Latest News
BWXT announces nuclear manufacturing plant expansion
BWX Technologies announced today plans to expand and add advanced manufacturing equipment to its manufacturing plant in Cambridge, Ontario, Canada.
A $36.3 million USD ($50M CAD) expansion will increase the plant’s size by 25 percent—to 280,000 square feet—and another $21.7 million USD ($30M CAD) will be spent on new equipment to increase and accelerate its output of large nuclear components. The investment will increase capacity and create more than 200 long-term jobs for skilled workers, engineers, and support staff, according to the company.
Diogo R. Ferreira, Pedro J. Carvalho, Horácio Fernandes, JET Contributors
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 47-56
Technical Paper | doi.org/10.1080/15361055.2017.1390386
Articles are hosted by Taylor and Francis Online.
Plasma tomography consists of reconstructing a two-dimensional radiation profile of a poloidal cross section of a fusion device based on line-integrated measurements along several lines of sight. The reconstruction process is computationally intensive, and in practice, only a few reconstructions are usually computed per pulse. In this work, we trained a deep neural network based on a large collection of sample tomograms that have been produced at JET over several years. Once trained, the network is able to reproduce those results with high accuracy. More importantly, it can compute all the tomographic reconstructions for a given pulse in just a few seconds. This makes it possible to visualize several phenomena—such as plasma heating, disruptions, and impurity transport—over the course of the entire pulse.