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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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NN Asks: What did you learn from ANS’s Nuclear 101?
Mike Harkin
When ANS first announced its new Nuclear 101 certificate course, I was excited. This felt like a course tailor-made for me, a transplant into the commercial nuclear world. I enrolled for the inaugural session held in November 2024, knowing it was going to be hard (this is nuclear power, of course)—but I had been working on ramping up my knowledge base for the past year, through both my employer and at a local college.
The course was a fast-and-furious roller-coaster ride through all the key components of the nuclear power industry, in one highly challenging week. In fact, the challenges the students experienced caught even the instructors by surprise. Thankfully, the shared intellectual stretch we students all felt helped us band together to push through to the end.
We were all impressed with the quality of the instructors, who are some of the top experts in the field. We appreciated not only their knowledge base but their support whenever someone struggled to understand a concept.
A. Kemal Ziver, Jonathan N. Carter, Christopher C. Pain, Cassiano R. E. de Oliveira, Antony J. H. Goddard, Richard S. Overton
Nuclear Technology | Volume 141 | Number 2 | February 2003 | Pages 122-141
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT03-A3355
Articles are hosted by Taylor and Francis Online.
A genetic algorithm (GA)-based optimizer (GAOPT) has been developed for in-core fuel management of advanced gas-cooled reactors (AGRs) at HINKLEY B and HARTLEPOOL, which employ on-load and off-load refueling, respectively. The optimizer has been linked to the reactor analysis code PANTHER for the automated evaluation of loading patterns in a two-dimensional geometry, which is collapsed from the three-dimensional reactor model. GAOPT uses a directed stochastic (Monte Carlo) algorithm to generate initial population members, within predetermined constraints, for use in GAs, which apply the standard genetic operators: selection by tournament, crossover, and mutation. The GAOPT is able to generate and optimize loading patterns for successive reactor cycles (multicycle) within acceptable CPU times even on single-processor systems. The algorithm allows radial shuffling of fuel assemblies in a multicycle refueling optimization, which is constructed to aid long-term core management planning decisions. This paper presents the application of the GA-based optimization to two AGR stations, which apply different in-core management operational rules. Results obtained from the testing of GAOPT are discussed.
