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Division Spotlight
Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
IAEA promoting nuclear energy with G20
The International Atomic Energy Agency launched a collaboration with the Group of 20 this week to highlight the key role that nuclear energy can play in achieving energy security and climate-change goals.
The aim of this first-of-its-kind partnership with G20—the world’s largest economic group—is to build momentum for nuclear power. This is the first time the IAEA has presented to G20 on issues relating to nuclear power.
R. Preuss, U von Toussaint
Fusion Science and Technology | Volume 69 | Number 3 | May 2016 | Pages 605-610
Technical Paper | doi.org/10.13182/FST15-178
Articles are hosted by Taylor and Francis Online.
Computer codes modeling plasma-wall interactions of fusion plasmas are costly in computer power and time—the running time for a single parameter setting is easily on the order of weeks or months, not to mention the expenditure for parametric studies. We propose to exploit the already gathered results in order to predict the outcome in the high-dimensional parameter space. For this, we utilize the Gaussian process method within the Bayesian framework. Uncertainties of the predictions are provided that point the way to parameter settings of further (expensive) simulations.