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Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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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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.
Satoshi Takeda, Takanori Kitada
Nuclear Science and Engineering | Volume 197 | Number 8 | August 2023 | Pages 1621-1633
Technical papers from: PHYSOR 2022 | doi.org/10.1080/00295639.2022.2123679
Articles are hosted by Taylor and Francis Online.
Assuming that the discrepancy between the experimental value and the calculation value comes from the cross section, experimental error, and calculation error, Bayesian estimation of the cross section and these errors were studied. Uncertainty of the discrepancy between the experimental value and the design value is discussed by comparing the present estimation and the bias factor method. Comparison of the formulas shows that the design value obtained by the bias factor method is consistent with that obtained by estimation of the cross section and calculation error of the target system. In addition, the uncertainty of the discrepancy between the experimental value and the design value can be reduced by considering a correlation of the experimental error between the mock-up experiment and the target system. A case study was performed using mixed oxide critical assembly benchmarks. The result shows that the experimental value of the target system can be accurately predicted by considering the cross section, experimental error, and calculation error.