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Conference Spotlight
2025 ANS Winter Conference & Expo
November 8–12, 2025
Washington, DC|Washington Hilton
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Fusion Science and Technology
Latest News
Nuclear News 40 Under 40: The wait is over
Following the enthusiastic response from the nuclear community in 2024 for the inaugural NN 40 Under 40, the Nuclear News team knew we had to take up the difficult task in 2025 of turning it into an annual event—though there was plenty of uncertainty as to how the community would receive a second iteration this year. That uncertainty was unfounded, clearly, as the tight-knit nuclear community embraced the chance to celebrate its up-and-coming generation of scientists, engineers, and policy makers who are working to grow the influence of this oft-misunderstood technology.
J. M. Canik, X.-Z. Tang
Fusion Science and Technology | Volume 71 | Number 1 | January 2017 | Pages 103-109
Technical Paper | doi.org/10.13182/FST16-124
Articles are hosted by Taylor and Francis Online.
While the sensitivity of the scrape-off layer and divertor plasma to the highly uncertain cross-field transport assumptions is widely recognized, the plasma is also sensitive to the details of the plasma-material interface (PMI) models used as part of comprehensive predictive simulations. Here, these PMI sensitivities are studied by varying the relevant submodels within the SOLPS plasma transport code. Two aspects are explored: the sheath model used as a boundary condition in SOLPS, and fast particle reflection rates for ions impinging on a material surface. Both of these have been the study of recent high-fidelity simulation efforts aimed at improving the understanding and prediction of these phenomena. It is found that in both cases quantitative changes to the plasma solution result from modification of the PMI model, with a larger impact in the case of the reflection coefficient variation. This indicates the necessities to better quantify the uncertainties within the PMI models themselves and to perform thorough sensitivity analysis to propagate these throughout the boundary model; this is especially important for validation against experiment, where the error in the simulation is a critical and less-studied piece of the code-experiment comparison.