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Nuclear Nonproliferation Policy
The mission of the Nuclear Nonproliferation Policy Division (NNPD) is to promote the peaceful use of nuclear technology while simultaneously preventing the diversion and misuse of nuclear material and technology through appropriate safeguards and security, and promotion of nuclear nonproliferation policies. To achieve this mission, the objectives of the NNPD are to: Promote policy that discourages the proliferation of nuclear technology and material to inappropriate entities. Provide information to ANS members, the technical community at large, opinion leaders, and decision makers to improve their understanding of nuclear nonproliferation issues. Become a recognized technical resource on nuclear nonproliferation, safeguards, and security issues. Serve as the integration and coordination body for nuclear nonproliferation activities for the ANS. Work cooperatively with other ANS divisions to achieve these objective nonproliferation policies.
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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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Can hydrogen be the transportation fuel in an otherwise nuclear economy?
Let’s face it: The global economy should be powered primarily by nuclear power. And it probably will by the end of this century, with a still-significant assist from renewables and hydro. Once nuclear systems are dominant, the costs come down to where gas is now; and when carbon emissions are reduced to a small portion of their present state, it will become obvious that most other sources are only good in niche settings. I mean, why use small modular reactors to load-follow when they can just produce that power instead of buffering it?
Miltiadis Alamaniotis, Andreas Ikonomopoulos, Lefteri H. Tsoukalas
Nuclear Technology | Volume 177 | Number 1 | January 2012 | Pages 132-145
Technical Paper | Nuclear Plant Operations and Control | doi.org/10.13182/NT12-A13333
Articles are hosted by Taylor and Francis Online.
Nuclear power plants are complex engineering systems comprised of many interacting and interdependent mechanical components whose failure might lead to degraded plant performance or unplanned shutdown with loss of power generation and negative economic impact. As a result, continuous component surveillance and accurate prediction of their failing points is necessary for their on-time replacement. In this paper, a probabilistic kernel approach for intelligent online monitoring of mechanical components is presented. Specifically, the probabilistic kernel notion of Gaussian processes (GPs) is applied to the distribution prediction of a component's degradation trend. The proposed method exploits the learning ability of a GP and updates its prediction using a feedback mechanism. The methodology is tested on actual turbine blade degradation data for a variety of topologies (i.e., kernels). The GP estimations are compared to those obtained with a nonprobabilistic, kernel-based machine learning algorithm, the support vector regression (SVR). The comparison outcome clearly demonstrates that GP prediction accuracy outperforms SVR in the majority of the cases while providing a predictive distribution instead of point estimates as SVR does.