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Division Spotlight
Nuclear Installations Safety
Devoted specifically to the safety of nuclear installations and the health and safety of the public, this division seeks a better understanding of the role of safety in the design, construction and operation of nuclear installation facilities. The division also promotes engineering and scientific technology advancement associated with the safety of such facilities.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
Standards Program
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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Latest News
MIT’s nuclear professional courses benefit United States—and now Australia too
Some 30 nuclear engineering departments at universities across the United States graduate more than 900 students every year. These young men and women are the present and future of the domestic nuclear industry as it seeks to develop and deploy advanced nuclear energy technologies, grow its footprint on the power grid, and penetrate new markets while continuing to run the existing fleet of reactors reliably and economically.
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.