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Fusion Energy
This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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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
Bipartisan Fusion Energy Act pushes for regulatory clarity
Padilla
Sen. Alex Padilla (D., Calif.) introduced the Fusion Energy Act (S. 4151) last month with a bipartisan group of cosponsors—John Cornyn (R., Texas), Cory Booker (D., N.J.), Todd Young (R., Ind.), and Patty Murray (D., Wash.). The legislation would codify the Nuclear Regulatory Commission’s regulatory authority over commercial fusion energy systems to streamline the creation of clear federal regulations that will support the development of commercial fusion power plants—and would require a report within one year on a study of risk- and performance-based, design-specific licensing frameworks for “mass-manufactured fusion machines.
“Congress must do everything in its power to ensure continued U.S. leadership in developing commercial fusion energy facilities,” said Padilla as he introduced the bill. “The Fusion Energy Act would provide regulatory certainty for investors as the NRC develops and streamlines frameworks for such facilities.”
Akio Yamamoto
Nuclear Technology | Volume 144 | Number 1 | October 2003 | Pages 63-75
Technical Paper | Nuclear Plant Operations and Control | doi.org/10.13182/NT03-A3429
Articles are hosted by Taylor and Francis Online.
In this paper, neural networks are used to predict core characteristics, and the predicted results are used to screen poor loading patterns in order to improve optimization efficiency. The radial peaking factor, cycle length, and maximum burnup through the cycle depletion calculations were evaluated by the neural network, and these core characteristics were used for screening. The screened loading patterns were evaluated by the core calculation code as ordinary in-core optimizations. The calculation results of the test problem indicated that the loading pattern screening using the neural network effectively improves the optimization results. Since the computation time for a cycle depletion calculation with the neural network is quite short, the computation load for the screening is negligible. Since the neural network is periodically retrained using the latest evaluation results of the core calculation code, its prediction accuracy is continuously improved during the optimization. The typical prediction accuracies of the radial peaking factor, cycle length, and maximum burnup in the latter part of the optimizations were 3 to 4%, 0.01 to 0.02 GWd/t, and 0.2 GWd/t, respectively, in the test problem. These accuracies are satisfactory for loading pattern screening.