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Division Spotlight
Robotics & Remote Systems
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.
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
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Mauricio Tano, Pablo Rubiolo (Univ of Grenoble-Alpes), Julien Giraud, Veronique Ghetta (LPSC, CNRS/IN2P3)
Proceedings | 2018 International Congress on Advances in Nuclear Power Plants (ICAPP 2018) | Charlotte, NC, April 8-11, 2018 | Pages 533-542
Inverse problem methods deal with the evaluation of the causal factors that result on a set of measurements or observations. Inverse problems found in nuclear reactors involve non-linear and coupled physical phenomena, making the causation effects complicated to de assessed. Furthermore, the extent of the experimental data collected is limited and this data is subjected to experimental noise. In the following paper, a method for solving inverse problems in nuclear reactors with coupled physical phenomena is developed. In the proposed approach, the inverse problem is solved through the minimization of a performance function. The minimization of this performance function is achieved with a preconditioned gradient descendent method. The generalized gradient of the performance function is obtained using the adjoint of the multiphysics equations of the system. Furthermore, for reducing the sensitivity to noise of the inverse problem, a preconditioner based in a Kalman Filter is developed. As an example, the methodology is applied for solving the inverse problem of finding the heat flux in the wall of a natural convection experiment.