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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Empowering the next generation: ANS’s newest book focuses on careers in nuclear energy
A new career guide for the nuclear energy industry is now available: The Nuclear Empowered Workforce by Earnestine Johnson. Drawing on more than 30 years of experience across 16 nuclear facilities, Johnson offers a practical, insightful look into some of the many career paths available in commercial nuclear power. To mark the release, Johnson sat down with Nuclear News for a wide-ranging conversation about her career, her motivation for writing the book, and her advice for the next generation of nuclear professionals.
When Johnson began her career at engineering services company Stone & Webster, she entered a field still reeling from the effects of the Three Mile Island incident in 1979, nearly 15 years earlier. Her hiring cohort was the first group of new engineering graduates the company had brought on since TMI, a reflection of the industry-wide pause in nuclear construction. Her first long-term assignment—at the Millstone site in Waterford, Conn., helping resolve design issues stemming from TMI—marked the beginning of a long and varied career that spanned positions across the country.
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.