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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.
Dan G. Cacuci, Erkan Arslan
Nuclear Science and Engineering | Volume 176 | Number 3 | March 2014 | Pages 339-349
Technical Paper | doi.org/10.13182/NSE13-31
Articles are hosted by Taylor and Francis Online.
This work applies the predictive modeling procedure formulated by Cacuci and Ionescu-Bujor [Nucl. Sci. Eng., Vol. 165, p. 18 (2010)] to assimilate experimental data from the international Organisation for Economic Co-operation and Development/U.S. Nuclear Regulatory Commission boiling water reactor full-size fine-mesh bundle test (BFBT) benchmarks to calibrate and reduce systematically and significantly the uncertainties in the predictions of the light water reactor thermal-hydraulic code FLICA4. The BFBT benchmarks were designed by the Nuclear Power Engineering Corporation of Japan for enabling systematic validation of thermal-hydraulic codes by using full-scale experimental data. This work specifically uses BFBT experimental data for the “pump trip for a high-burnup assembly” in the predictive modeling formalism to calibrate parameters and time-dependent boundary conditions (power, mass flow rates, and outlet pressure distributions) in FLICA4, yielding best-estimate predictions of axial void fraction distributions. The resulting uncertainties for the best-estimate time-dependent model parameters and void fraction response distributions are shown to be smaller than the a priori experimental and computed uncertainties, thus demonstrating the successful use of predictive modeling for the large-scale reactor analysis code FLICA4 using BFBT benchmark-grade experiments.