ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Feb 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
February 2026
Nuclear Technology
January 2026
Fusion Science and Technology
Latest News
INL’s Teton supercomputer open for business
Idaho National Laboratory has brought its newest high‑performance supercomputer, named Teton, online and made it available to users through the Department of Energy’s Nuclear Science User Facilities program. The system, now the flagship machine in the lab’s Collaborative Computing Center, quadruples INL’s total computing capacity and enters service as the 85th fastest supercomputer in the world.
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.