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Nuclear Science and Engineering
Fusion Science and Technology
A day in the life of the nuclear community
The November issue of Nuclear News is focused on the individuals who make up our nuclear community.
We invited a small group of those individuals to tell us about their day-to-day work in some of the many occupations and applications of nuclear science and technology, and they responded generously. They were ready to tell us about the part they play, together with colleagues and team members, in supplying clean energy, advancing technology, protecting safety and health, and exploring fundamental science.
In these pages, we see a community that can celebrate both those workdays that record progress moving at a steady pace and the exceptional days when a goal is reached, a briefing is delivered, a contract goes through, a discovery is made, or an unforeseen challenge is overcome.
The Nuclear News staff hopes that you enjoy meeting these members of our community—or maybe get reacquainted with friends—through their words and photos.
Sophie Blondel, David E. Bernholdt, Karl D. Hammond, Lin Hu, Dimitrios Maroudas, Brian D. Wirth
Fusion Science and Technology | Volume 71 | Number 1 | January 2017 | Pages 84-92
Technical Paper | dx.doi.org/10.13182/FST16-109
Articles are hosted by Taylor and Francis Online.
We present a hierarchical multiscale modeling study of implanted helium (He) segregation near grain boundaries (GBs) of tungsten. We extend our spatially dependent cluster dynamics model to two spatial dimensions in order to take into account the biased drift of mobile He clusters toward the GBs observed in atomic-scale simulations. We are able to reproduce the results from large-scale molecular dynamics simulations near and away from the GBs at low fluence with the extended cluster dynamics model. We suggest and verify that the sink (surface and GB) strengths are attenuated by the increasing concentration of He clusters at high fluence. This cluster dynamics model continues to set the stage for development of fully atomistically informed, coarse-grained models for computationally efficient predictions of He retention and surface morphological evolution, advancing progress toward the goal of efficient and optimal design of plasma-facing components.