ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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!
Latest Magazine Issues
Sep 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
September 2025
Nuclear Technology
Fusion Science and Technology
October 2025
Latest News
A wave of new U.S.-U.K. deals ahead of Trump’s state visit
President Trump will arrive in the United Kingdom this week for a state visit that promises to include the usual pomp and ceremony alongside the signing of a landmark new agreement on U.S.-U.K. nuclear collaboration.
B. T. Rearden, M. L. Williams, M. A. Jessee, D. E. Mueller, D. A. Wiarda
Nuclear Technology | Volume 174 | Number 2 | May 2011 | Pages 236-288
Technical Paper | Special Issue on the SCALE Nuclear Analysis Code System / Radiation Protection | doi.org/10.13182/NT174-236
Articles are hosted by Taylor and Francis Online.
In SCALE 6, the Tools for Sensitivity and UNcertainty Analysis Methodology Implementation (TSUNAMI) modules calculate the sensitivity of keff or reactivity differences to the neutron cross-section data on an energy-dependent, nuclide-reaction-specific basis. These sensitivity data are useful for uncertainty quantification, using the comprehensive neutron cross-section-covariance data in SCALE 6. Additional modules in SCALE 6 use the sensitivity and uncertainty data to produce correlation coefficients and other relational parameters that quantify the similarity of benchmark experiments to application systems for code validation purposes. Bias and bias uncertainties are quantified using parametric trending analysis or data adjustment techniques, providing detailed assessments of sources of biases and their uncertainties and quantifying gaps in experimental data available for validation. An example application of these methods is presented for a generic burnup credit cask model.