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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.
Binh T. Pham, Grant L. Hawkes, Jeffrey J. Einerson
Nuclear Technology | Volume 196 | Number 2 | November 2016 | Pages 396-407
Technical Paper | doi.org/10.13182/NT16-31
Articles are hosted by Taylor and Francis Online.
This paper presents the quantification of uncertainty of the calculated temperature data for the Advanced Gas Reactor (AGR) fuel irradiation experiments conducted in the Advanced Test Reactor at Idaho National Laboratory in support of the Advanced Reactor Technologies Fuel Development and Qualification Program. The predicted temperatures with associated uncertainty for AGR tests using the ABAQUS finite element heat transfer code are used to validate the fission product transport and fuel performance simulation models. To quantify the uncertainty of calculated temperatures, this study identifies and analyzes model parameters of potential importance to the predicted fuel temperatures. The selection of input parameters for uncertainty quantification is based on the ranking of their influence on the variation of temperature predictions. Thus, selected input parameters include those with high sensitivity and those with large uncertainty. The propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of the calculated temperatures. The sensitivity analysis performed in this work went beyond the traditional local sensitivity. Using an experimental design, an analysis of pairwise interactions of model parameters was performed to establish the sufficiency of the first-order (linear) expansion terms in constructing the response surface. To achieve completeness, the uncertainty propagation made use of pairwise noise correlations of model parameters. The AGR-2 overall fuel temperature uncertainties reported here are less than 5% (or 60°C).