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May 31–June 3, 2026
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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.
Jie Lin, Yair Bartal, Robert E. Uhrig
Nuclear Technology | Volume 111 | Number 1 | July 1995 | Pages 46-62
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT95-A35143
Articles are hosted by Taylor and Francis Online.
The importance of automatic diagnostic systems for nuclear power plants (NPPs) has been discussed in numerous studies, and various such systems have been proposed. None of those systems were designed to predict the severity of the diagnosed scenario. A classification and severity prediction system for NPP transients is developed. The system is based on nearest neighbors modeling, which is optimized using genetic algorithms. The optimization process is used to determine the most important variables for each of the transient types analyzed. An enhanced version of the genetic algorithms is used in which a local downhill search is performed to further increase the accuracy achieved. The genetic algorithms search was implemented on a massively parallel supercomputer, the KSR1-64, to perform the analysis in a reasonable time. The data for this study were supplied by the highfidelity simulator of the San Onofre unit 1 pressurized water reactor.
