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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.
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Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
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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ANS standard updated for determining meteorological information at nuclear facilities
Following approval in October from the American National Standards Institute, ANSI/ANS-3.11-2024, Determining Meteorological Information at Nuclear Facilities, was published in late November. This standard provides criteria for gathering, assembling, processing, storing, and disseminating meteorological information at commercial nuclear power plants, U.S. Department of Energy/National Nuclear Security Administration nuclear facilities, and other national or international nuclear facilities.
Jinkai Wang, Warren D. Reece
Nuclear Science and Engineering | Volume 167 | Number 2 | February 2011 | Pages 154-164
Technical Paper | doi.org/10.13182/NSE09-94
Articles are hosted by Taylor and Francis Online.
The relative yields of delayed neutrons and the half-lives of their precursor nuclei are usually determined indirectly by the least-squares method based on the differences between experimental and fitted data. It is noted that the recommended values from ENDF/B-VII, ENDF/B-VI.8, JENDL-3.3, JEF-2.2, and JEFF-3.1 are significantly different. To evaluate these parameters, the measured data sets used in this research were simulated by the Monte Carlo method, and they were strict Poisson distributed data generated from Keepin's six-group data. Three different numerical methods (matrix inverse with singular value decomposition, Levenberg-Marquardt, and quasi Newton) with different regularization techniques were applied to estimate the parameter values. The fitted results were proven to be very unstable, and their calculated results were very different even for the same data set. Further investigation found ill-conditioned problems to be the reason for this instability. A better numerical method was suggested in this research.