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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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Latest News
Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Brian A. Lockwood, Mihai Anitescu
Nuclear Science and Engineering | Volume 170 | Number 2 | February 2012 | Pages 168-195
Technical Paper | doi.org/10.13182/NSE10-86
Articles are hosted by Taylor and Francis Online.
In this work, we investigate the issue of providing a statistical model for the response of a computer model-described nuclear engineering system, for use in uncertainty propagation. The motivation behind our approach is the need for providing an uncertainty assessment even in the circumstances where only a few samples are available. Building on our recent work in using a regression approach with derivative information for approximating the system response, we investigate the ability of a universal gradient-enhanced Kriging model to provide a means for inexpensive uncertainty quantification. The universal Kriging model can be viewed as a hybrid of polynomial regression and Gaussian process regression. For this model, the mean behavior of the surrogate is determined by a polynomial regression, and deviations from this mean are represented as a Gaussian process. Tests with explicit functions and nuclear engineering models show that the universal gradient-enhanced Kriging model provides a more accurate surrogate model than either regression or ordinary Kriging models. In addition, we investigate the ability of the Kriging model to provide error predictions and bounds for regression models.