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.
Division Spotlight
Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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
Apr 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
May 2025
Nuclear Technology
April 2025
Fusion Science and Technology
Latest News
General Kenneth Nichols and the Manhattan Project
Nichols
The Oak Ridger has published the latest in a series of articles about General Kenneth D. Nichols, the Manhattan Project, and the 1954 Atomic Energy Act. The series has been produced by Nichols’ grandniece Barbara Rogers Scollin and Oak Ridge (Tenn.) city historian David Ray Smith. Gen. Nichols (1907–2000) was the district engineer for the Manhattan Engineer District during the Manhattan Project.
As Smith and Scollin explain, Nichols “had supervision of the research and development connected with, and the design, construction, and operation of, all plants required to produce plutonium-239 and uranium-235, including the construction of the towns of Oak Ridge, Tennessee, and Richland, Washington. The responsibility of his position was massive as he oversaw a workforce of both military and civilian personnel of approximately 125,000; his Oak Ridge office became the center of the wartime atomic energy’s activities.”
Jeongwon Seo, Hany Abdel-Khalik, Zoltan Perko
Nuclear Technology | Volume 206 | Number 12 | December 2020 | Pages 1827-1839
Technical Paper | doi.org/10.1080/00295450.2020.1721407
Articles are hosted by Taylor and Francis Online.
This paper presents an algorithm for completing sensitivity analysis that respects linear constraints placed on the associated model’s input parameters. Any sensitivity analysis (linear or nonlinear, local or global) focuses on measuring the impact of input parameter variations on model responses of interest, which may require the analyst to execute the model numerous times with different model parameter perturbations. With the constraints present, the degrees of freedom available for input parameter variations are reduced, and hence any analysis that changes model parameters must respect these constraints. Focusing here on linear constraints, earlier work has shown that constraints may be respected in many ways, causing ambiguities, i.e., nonuniqueness, in the results of a sensitivity analysis, forcing the analyst to introduce dependencies with downstream analyses, e.g., uncertainty quantification, that employ the sensitivity analysis results. This paper develops the theoretical details for a new algorithm to select model parameter variations that automatically satisfy linear constraints resulting in unique results for the sensitivity analysis, thereby removing any custom dependencies with downstream analyses. To demonstrate the performance of the algorithm, it is applied to solve the multigroup eigenvalue problem for the multiplication factor in a representative CANDU core-wide model. The model parameters analyzed are the group prompt neutron fractions, whose summation must be equal to one over all energy groups. The results indicate that the new algorithm identifies the gradient direction uniquely which represents the direction of maximum change while satisfying the constraints, thus removing any ambiguities resulting from the constraints as identified by earlier work.