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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.
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International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
S. I. Chang, T. W. Kerlin
Nuclear Science and Engineering | Volume 64 | Number 2 | October 1977 | Pages 673-683
Technical Paper | doi.org/10.13182/NSE77-A27399
Articles are hosted by Taylor and Francis Online.
Large linear dynamic models for nuclear reactor systems are widely used for simulation and control system design. It is important to be able to verify these models and the parameters in them. Existing parameter identification techniques are very time consuming for use with large systems. In this study, identification is achieved by an optimization procedure that adjusts system parameters to minimize differences between experimental frequency responses and theoretical frequency responses obtained from the dynamic model. A new method that uses a partitioned martrix technique was developed. This technique constitutes a very efficient analysis algorithm for large models when implemented on the digital computer. The work included a study of methods for assessing the identifiability of parameters by fitting dynamic test data. The Fisher information matrix was found to be useful for this purpose. It was also found that the frequency dependency of the sensitivity function is important in determining identifiability. The measurements should include frequencies where the sensitivity to the parameter of interest is largest. Also, it was found that separate, unique identification of parameters with parallel curves of sensitivity versus frequency is impossible regardless of how large the magnitudes of the sensitivities are. The method was demonstrated in a test case. It used data (from the Oconee I pressurized water reactor) and a 29th-order model. The results demonstrated that the computational requirements are reasonable for large systems and that the procedure can identify parameters if all the necessary conditions are satisfied. In general, the work has provided a systematic method for parameter identification in systems described by large linear dynamic models.