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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.
Scott A. Comes, Paul J. Turinsky
Nuclear Technology | Volume 83 | Number 1 | October 1988 | Pages 31-48
Technical Paper | Fuel Cycle | doi.org/10.13182/NT88-A34173
Articles are hosted by Taylor and Francis Online.
A methodology has been developed for determining the family of near-optimum fuel management schemes that minimize the levelized fuel cycle costs of a light water reactor over a multicycle planning horizon. Feed batch enrichments and sizes, burned batches to reinsert, and burnable poison loadings are determined for each cycle in the planning horizon. Flexibility in the methodology includes the capability to assess the economic benefits of various partially burned batch reload strategies as well as the effects of using split feed enrichments and enrichment palettes. Constraint limitations are imposed on feed enrichments, discharge burnups, moderator temperature coefficient, and cycle energy requirements. The methodology, incorporated into a code named OCEON, uses a zero-dimensional reactor physics model and a rapid fuel cycle cost routine to select minimum cost cycling schemes that satisfy all constraints. These candidate schemes are then examined with a two-dimensional nodal reactor physics model to more accurately calculate feed enrichments, batch burnups, and fuel cycle costs. The use of Monte Carlo integer programming to direct the optimization process allows for the determination of a family of low cost schemes from which the fuel manager can select the strategy that best fits his needs.