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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
X-energy receives federal tax credit for TRISO fuel facility
Advanced reactor company X-energy has been awarded $148.5 million in tax credits under the Inflation Reduction Act for construction of its TRISO-X fuel fabrication facility in Oak Ridge, Tenn.
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.