ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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!
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March 2025
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February 2025
Latest News
ARG-US Remote Monitoring Systems: Use Cases and Applications in Nuclear Facilities and During Transportation
As highlighted in the Spring 2024 issue of Radwaste Solutions, researchers at the Department of Energy’s Argonne National Laboratory are developing and deploying ARG-US—meaning “Watchful Guardian”—remote monitoring systems technologies to enhance the safety, security, and safeguards (3S) of packages of nuclear and other radioactive material during storage, transportation, and disposal.
Toshihiro Yamamoto, Hiroki Sakamoto
Nuclear Science and Engineering | Volume 198 | Number 8 | August 2024 | Pages 1607-1619
Research Article | doi.org/10.1080/00295639.2023.2266623
Articles are hosted by Taylor and Francis Online.
The inverse reactor period α is a fundamental mode eigenvalue of the α-mode nonlinear Boltzmann eigenvalue equation that considers delayed neutron contributions. Thus far, several Monte Carlo methods, including the α-k, weight balancing, and transition rate matrix methods, have been developed to calculate α. This study presents a new Monte Carlo method for predicting α by using the derivatives of the k-eigenvalue with respect to α. Formulae are derived to calculate the first and second derivatives using the differential operator sampling method. The key feature of the new proposed method is its ability to estimate the uncertainty of the predicted α by considering the uncertainty of the k-eigenvalue and its derivatives with respect to α.