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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.
C. J. Solomon, A. Sood, T. E. Booth, J. K. Shultis
Nuclear Science and Engineering | Volume 176 | Number 1 | January 2014 | Pages 1-36
Technical Paper | doi.org/10.13182/NSE12-81
Articles are hosted by Taylor and Francis Online.
A method for deterministically minimizing the cost of a single Monte Carlo tally employing weight-dependent weight-window variance reduction has been developed. This method relies on deterministic calculations of the tally's variance and average computational time per history, the product of which is the cost (inverse figure of merit) of the tally calculation. The tally's variance is deterministically computed by solving the history-score moment equations that describe the moments of the tally's score distribution, and the average time per history is computed by solving the future time equation that describes the expected amount of computational time a particle and its progeny require to process to termination. Both equations are solved by the Sn method. Results are presented for one- and two-dimensional problems that demonstrate increased calculation efficiency, by factors of 1.1 to 2, of the optimized problems over standard adjoint (importance) biasing.
