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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Powering the future: How the DOE is fueling nuclear fuel cycle research and development
As global interest in nuclear energy surges, the United States must remain at the forefront of research and development to ensure national energy security, advance nuclear technologies, and promote international cooperation on safety and nonproliferation. A crucial step in achieving this is analyzing how funding and resources are allocated to better understand how to direct future research and development. The Department of Energy has spearheaded this effort by funding hundreds of research projects across the country through the Nuclear Energy University Program (NEUP). This initiative has empowered dozens of universities to collaborate toward a nuclear-friendly future.
A. Galperin, E. Nissan
Nuclear Science and Engineering | Volume 99 | Number 4 | August 1988 | Pages 343-352
Technical Paper | doi.org/10.13182/NSE88-A23563
Articles are hosted by Taylor and Francis Online.
A computerized heuristic search method for the generation and optimization of fuel reload configurations is proposed and investigated. The heuristic knowledge is expressed modularly in the form of “IF-THEN”production rules. The method was implemented in a program coded in the Franz LISP programming language and executed under the UNIX operating system. A test problem was formulated, based on a typical light water reactor reload problem with a few simplifications assumed, in order to allow formulation of the reload strategy into a relatively small number of rules. A computer run of the problem was performed with a VAX-780 machine. A set of 312 solutions was generated in ∼20 min of execution time. Testing of a few arbitrarily chosen configurations demonstrated reasonably good performance for the computer-generated solutions. A computerized generator of reload configurations may be used for the fast generation or modification of reload patterns and as a tool for the formulation, tuning, and testing of the heuristic knowledge rules used by an “expert” fuel manager.