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Conference Spotlight
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.
Dirk-A. Becker, Sabine M. Spiessl (GRS)
Proceedings | 16th International High-Level Radioactive Waste Management Conference (IHLRWM 2017) | Charlotte, NC, April 9-13, 2017 | Pages 347-355
For deep geological repositories for radioactive waste, the release of contaminants to the biosphere has to be assessed in advance, which can only be done by modelling all relevant effects in an integrated, coupled model. Such computation models are typically rather complex, as they combine a lot of influences from various processes. As a result, they often show a highly non-linear behavior.
As there are many uncertainties influencing the calculation results, sensitivity analysis is an important tool for investigating the model behavior. It is not only appropriate for directing research activities, but can contribute essentially to a proper model understanding and even reveal errors in the model or the data.
Uncritical application of standard methods can lead to correct sensitivity estimation, but it is also possible that it yields misleading results, jeopardizing the benefit of sensitivity analysis. The research program presented here was set up in order to investigate standard methods as well as new developments in sensitivity analysis and test their applicability to performance assessment model results. The final goal of the investigations was to provide some guidance to a modeler for performing an effective and meaningful sensitivity analysis.
Three performance assessment models, describing hypothetical repositories for radioactive waste in different geological formations, were defined. These models show different particularities that are typical for their specific type, like output results spread over many orders of magnitude, occurrence of a considerable number of zero-runs, a two-split output distribution or a nearly non-continuous behavior. For each model a set of uncertain input parameters with plausible probability density functions (pdfs) was defined.
The models were calculated numerous times, using parameter samples of different sizes and based on different sampling algorithms. A variety of different standard and advanced methods of numerical sensitivity analysis were applied. Some experiments were done with correlated input parameters and transformation of model output. Moreover, graphical methods of sensitivity analysis were applied.
The assessment was oriented at the following questions: - How robust are the results? Do they considerably change if a different sample of same size is used? How many runs are necessary to achieve stable results? - Do the different sensitivity measures and graphical methods qualitatively agree about the main sensitivities? - Are the results plausible and understandable? - Are all sensitivities detected by the different methods? - Which sampling algorithm seems best? - Can the significance of sensitivity analysis be improved by transforming the model output to a more appropriate scale? - How numerically effective are the different methods of sensitivity analysis?
