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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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A webinar, and a new opportunity to take ANS’s CNP Exam
Applications are now open for the fall 2025 testing period for the American Nuclear Society’s Certified Nuclear Professional (CNP) exam. Applications are being accepted through October 14, and only three testing sessions are offered per year, so it is important to apply soon. The test will be administered from November 12 through December 16. To check eligibility and schedule your exam, click here.
In addition, taking place tomorrow (September 19) from 12:00 noon to 1:00 p.m. (CDT), ANS will host a new webinar, “How to Become a Certified Nuclear Professional.” More information is available below in this article.
Emerald D. Ryan, Chad L. Pope
Nuclear Technology | Volume 206 | Number 10 | October 2020 | Pages 1506-1516
Technical Paper | doi.org/10.1080/00295450.2019.1704576
Articles are hosted by Taylor and Francis Online.
Flooding is a hazard for nuclear power plants (NPPs) and has caused extensive damage and economic impact. Improved NPP flooding risk characterization starts with improving scenario realism by using physics-based flooding simulations. Smoothed particle hydrodynamics (SPH) is one method for modeling fluid flow and is being investigated for NPP flooding simulation. While still in its infancy as a fluid simulation tool, SPH offers enticing features especially in three-dimensional modeling. However, when conducting SPH simulations, users must establish, inter alia, the appropriate particle spacing, which can be a tedious and time-consuming process. This paper describes the coupling of the SPH code Neutrino and the Idaho National Laboratory developed Risk Analysis Virtual Environment (RAVEN). By coupling Neutrino and RAVEN, the RAVEN optimization capabilities can now be applied to the particle spacing selection problem. A brief description of SPH, the overall capabilities of RAVEN, and the protocol used to couple the codes are provided. Additionally, the paper details a hypothetical problem and demonstrates the ability of automating the particle spacing selection and performing an example particle spacing optimization using RAVEN. With the Neutrino/RAVEN coupling established, a wide range of capabilities can now be utilized including optimization, reduced order model training and analysis, uncertainty quantification, sensitivity analysis, etc. Previously, these capabilities would require extensive work and time from the Neutrino user. Now, these capabilities are readily available and require only the creation of a RAVEN input file.