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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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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.
A. Sarkar, S. K. Sinha, J. K. Chakravartty, R. K. Sinha
Nuclear Technology | Volume 181 | Number 3 | March 2013 | Pages 459-465
Technical Papers | Fuel Cycle and Management/Materials for Nuclear Systems | doi.org/10.13182/NT13-A15803
Articles are hosted by Taylor and Francis Online.
A model is developed to predict the in-reactor dimensional changes of the pressure tube materials in Indian pressurized heavy water power reactors (PHWRs) using artificial neural networks (ANNs). The inputs of the ANN are the alloy composition of the tube (concentration of Nb, O, N, and Fe), mechanical properties (yield strength, ultimate tensile strength, and percentage elongation), tube thickness, temperature, and fluence whereas axial elongation is the output. Measured elongation data from various tubes used in Indian PHWRs at Rajasthan Atomic Power Station (RAPS 4) are employed to develop the model. A three-layer feed-forward ANN is trained with the Levenberg-Marquardt training algorithm. It has been shown that the developed ANN model can efficiently and accurately predict the axial elongation of pressure tubes. The results show the high significance of Fe concentration and irradiation fluence in determining axial elongation.
