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Nuclear Nonproliferation Policy
The mission of the Nuclear Nonproliferation Policy Division (NNPD) is to promote the peaceful use of nuclear technology while simultaneously preventing the diversion and misuse of nuclear material and technology through appropriate safeguards and security, and promotion of nuclear nonproliferation policies. To achieve this mission, the objectives of the NNPD are to: Promote policy that discourages the proliferation of nuclear technology and material to inappropriate entities. Provide information to ANS members, the technical community at large, opinion leaders, and decision makers to improve their understanding of nuclear nonproliferation issues. Become a recognized technical resource on nuclear nonproliferation, safeguards, and security issues. Serve as the integration and coordination body for nuclear nonproliferation activities for the ANS. Work cooperatively with other ANS divisions to achieve these objective nonproliferation policies.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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Latest News
First astatine-labeled compound shipped in the U.S.
The Department of Energy’s National Isotope Development Center (NIDC) on March 31 announced the successful long-distance shipment in the United States of a biologically active compound labeled with the medical radioisotope astatine-211 (At-211). Because previous shipments have included only the “bare” isotope, the NIDC has described the development as “unleashing medical innovation.”
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.