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Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
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April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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.”
Man Gyun Na, Belle R. Upadhyaya, Xiaojia Xu, In Joon Hwang
Nuclear Science and Engineering | Volume 154 | Number 3 | November 2006 | Pages 353-366
Technical Paper | doi.org/10.13182/NSE06-A2638
Articles are hosted by Taylor and Francis Online.
In this paper, a space reactor core dynamics is identified online by a recursive least-squares method. Based on this identified reactor model consisting of the control reactivity and the thermal electric generator power, the future thermoelectric (TE) generator power is predicted. A model predictive control method is applied to design an automatic controller for TE generator power control for a space reactor of the SP-100 system. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted TE generator power and the desired power and the variation of the control reactivity. Also, the control constraints are subjected to maximum and minimum reactivity and to maximum reactivity change. Therefore, the genetic algorithm that is appropriate to accomplish multiple objectives is used to optimize the model predictive controller. A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed controller. The results of numerical simulation to check the performance of the proposed controller show that the TE generator power level controlled by the proposed controller could track the target power level effectively, satisfying all control constraints.