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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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NN Asks: What did you learn from ANS’s Nuclear 101?
Mike Harkin
When ANS first announced its new Nuclear 101 certificate course, I was excited. This felt like a course tailor-made for me, a transplant into the commercial nuclear world. I enrolled for the inaugural session held in November 2024, knowing it was going to be hard (this is nuclear power, of course)—but I had been working on ramping up my knowledge base for the past year, through both my employer and at a local college.
The course was a fast-and-furious roller-coaster ride through all the key components of the nuclear power industry, in one highly challenging week. In fact, the challenges the students experienced caught even the instructors by surprise. Thankfully, the shared intellectual stretch we students all felt helped us band together to push through to the end.
We were all impressed with the quality of the instructors, who are some of the top experts in the field. We appreciated not only their knowledge base but their support whenever someone struggled to understand a concept.
Troy L. Becker, Allan B. Wollaber, Edward W. Larsen
Nuclear Science and Engineering | Volume 155 | Number 2 | February 2007 | Pages 155-167
Technical Paper | Mathematics and Computation, Supercomputing, Reactor Physics and Nuclear and Biological Applications | doi.org/10.13182/NSE07-A2653
Articles are hosted by Taylor and Francis Online.
A new hybrid Monte Carlo-Deterministic technique is presented for simulating global particle transport problems, in which flux estimates are desired at all physical locations in the system. This technique has two steps: First, an inexpensive deterministic global estimate of the forward flux is obtained; then Monte Carlo is used to estimate the multiplicative correction to the deterministic flux estimate. We call the multiplicative correction to the deterministic flux the correcton flux, and the Monte Carlo particles that estimate this flux correctons. For deep-penetration problems, the correcton flux has significantly less spatial variation than the physical flux. Therefore, the Monte Carlo process automatically distributes correctons much more uniformly across the system than it distributes Monte Carlo particles for the original angular flux. In the "deep" parts of the problem, at locations far from the source, this results in a greatly reduced variance and a greatly increased figure of merit.