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2026 Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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What’s the most difficult question you’ve been asked as a maintenance instructor?
Blye Widmar
"Where are the prints?!"
This was the final question in an onslaught of verbal feedback, comments, and critiques I received from my students back in 2019. I had two years of instructor experience and was teaching a class that had been meticulously rehearsed in preparation for an accreditation visit. I knew the training material well and transferred that knowledge effectively enough for all the students to pass the class. As we wrapped up, I asked the students how they felt about my first big system-level class, and they did not hold back.
“Why was the exam from memory when we don’t work from memory in the plant?” “Why didn’t we refer to the vendor documents?” “Why didn’t we practice more on the mock-up?” And so on.
Juan José Ortiz, Alejandro Castillo, José Luis Montes, Raúl Perusquía
Nuclear Science and Engineering | Volume 157 | Number 2 | October 2007 | Pages 236-244
Technical Note | doi.org/10.13182/NSE07-A2725
Articles are hosted by Taylor and Francis Online.
A new system to optimize both control rod pattern and fuel-loading design in boiling water reactors is shown. The system is named OCOTH, and it is based on heuristic techniques such as genetic algorithms, neural networks, and ant colonies. Each heuristic technique is used to design a part of the optimization process. So, the neural network finds an initial fuel loading with a Haling burnup calculation. The ant colony system optimizes full-power control rod pattern of the initial fuel loading. Finally, the genetic algorithm optimizes fuel loading with the optimized control rod patterns. The ant colony system and the genetic algorithm perform an iterative loop until a stop criterion is fulfilled; for example, control rod pattern and fuel-loading convergence. The OCOTH system was tested in an equilibrium cycle of Mexico's Laguna Verde Nuclear Power Plant. We found very good results in control rod pattern and fuel-loading coupled optimization.