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Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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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.
Laurian Dinca, Tunc Aldemir
Nuclear Science and Engineering | Volume 127 | Number 2 | October 1997 | Pages 199-219
Technical Paper | doi.org/10.13182/NSE97-A28597
Articles are hosted by Taylor and Francis Online.
A model-based parameter estimation method for nonlinear systems that does not require the linearization of the system equations and that can account for uncertainties in the monitored data as well as the parameters (e.g., random variations) is described. The method is particularly suitable for fault diagnosis because of its capability to assign probabilities of occurrence to user-specified parameter magnitude intervals that may be associated with system faults. The method regards system evolution in time as transitions between these intervals as well as user-specified magnitude intervals of the dynamic variables. These transition rates are obtained on-line from the system model and the monitored dynamic variable data and constitute a Markov chain in discrete time. The method then compares predicted and observed data at a given time step to narrow the estimated parameter range in the next time step. Implementations using a second-order van der Pol oscillator and a third-order system describing temporal xenon oscillations in a hypothetical reactor indicate that the method is computationally efficient and can be used for multiparameter estimation with incomplete information on the system state.