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Division Spotlight
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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
PG&E launches AI solution at Diablo Canyon
Diablo Canyon will host a commercial installation of the first on-site generative artificial intelligence deployment at a U.S. nuclear plant.
Pacific Gas & Electric is deploying Atomic Canyon’s Neutron Enterprise to assist the utility’s management of datasets associated with operations of Diablo Canyon. The software, which runs on Nvidia’s full-stack AI platform, enables intelligent document processing, computation of semantic embeddings, and generative capabilities. Its infrastructure allows nuclear facilities to process and analyze vast amounts of complex documentation with unprecedented speed and accuracy, according to the company.
K. Forsberg, Ning He, A. R. Massih
Nuclear Science and Engineering | Volume 122 | Number 1 | January 1996 | Pages 142-150
Technical Note | doi.org/10.13182/NSE96-A28555
Articles are hosted by Taylor and Francis Online.
Distribution of some important fuel rod performance parameters, internal rod pressure, and fission gas release in a boiling water reactor are studied using the quasi-Monte Carlo (QMC) probabilistic method. Rod power histories and important fabrication parameters are considered. The deterministic fuel performance code STAV6 together with a QMC pre- and postprocessor are used in the analysis. The convergence rate of the QMC method is considerably higher than the standard Monte Carlo method, which saves a substantial amount of computer time. Asymptotically, the error for QMC is proportional to 1/N, and for Monte Carlo, it is essentially proportional to 1/ where N is the number of calculations (computer runs). Principles of the QMC method are discussed, and an algorithm to generate such data is outlined.