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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.
Timothy P. Burke, Brian C. Kiedrowski, William R. Martin
Nuclear Science and Engineering | Volume 188 | Number 2 | November 2017 | Pages 109-139
Technical Paper | doi.org/10.1080/00295639.2017.1350000
Articles are hosted by Taylor and Francis Online.
Kernel density estimators (KDEs) are applied to estimate neutron scalar flux and reaction rate densities in Monte Carlo neutron transport simulations of heterogeneous nuclear reactors in continuous energy. The mean free path (MFP) KDE is introduced in order to handle the issues that arise from estimating the discontinuous reaction rate densities at material interfaces. Results show the MFP KDE is more accurate at estimating reaction rates compared with previous KDE formulations. An approximate MFP (aMFP) KDE is introduced to circumvent several practical issues presented by the MFP KDE. A volume-averaged KDE is derived and used to determine the bias introduced by the aMFP KDE. A KDE is formulated for cylindrical coordinates to better represent the geometry and capture the physics in two-dimensional reactor physics problems. The results indicate that the cylindrical MFP KDE and cylindrical aMFP KDE are accurate tools for capturing reaction rates in heterogeneous reactor physics problems in continuous energy, with local biases of less than 1%.