Abstract
This article presents a joint study between computer scientists and fusion scientists in developing visual tools for studying patterns in flow fields from large-scale magnetic confinement fusion simulations. The authors visualize time-varying flow data by generating trajectory curves via massless particle advection and design a set of color functions, pathline filters, and projection methods specific to achieve fusion research objectives. These tools aid scientists in managing the visual complexity of large trajectory datasets and are crucial in locating and understanding subtle features of interest. The authors demonstrate the effectiveness of their techniques by using real fusion simulation data and provide insight by domain scientists. They also discuss how their methods address common scalability concerns.
| Original language | English (US) |
|---|---|
| Article number | 7274254 |
| Pages (from-to) | 68-77 |
| Number of pages | 10 |
| Journal | Computing in Science and Engineering |
| Volume | 18 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 1 2016 |
| Externally published | Yes |
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Keywords
- fusion simulations
- particle advection
- particle data
- scientific computing
- trajectories
- visualization
ASJC Scopus subject areas
- Computer Science(all)
- Engineering(all)
Cite this
Visualization Techniques for Studying Large-Scale Flow Fields from Fusion Simulations. / Sauer, Franz; Zhang, Yubo; Wang, Weixing; Ethier, Stephane; Ma, Kwan-Liu.
In: Computing in Science and Engineering, Vol. 18, No. 2, 7274254, 01.03.2016, p. 68-77.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Visualization Techniques for Studying Large-Scale Flow Fields from Fusion Simulations
AU - Sauer, Franz
AU - Zhang, Yubo
AU - Wang, Weixing
AU - Ethier, Stephane
AU - Ma, Kwan-Liu
PY - 2016/3/1
Y1 - 2016/3/1
N2 - This article presents a joint study between computer scientists and fusion scientists in developing visual tools for studying patterns in flow fields from large-scale magnetic confinement fusion simulations. The authors visualize time-varying flow data by generating trajectory curves via massless particle advection and design a set of color functions, pathline filters, and projection methods specific to achieve fusion research objectives. These tools aid scientists in managing the visual complexity of large trajectory datasets and are crucial in locating and understanding subtle features of interest. The authors demonstrate the effectiveness of their techniques by using real fusion simulation data and provide insight by domain scientists. They also discuss how their methods address common scalability concerns.
AB - This article presents a joint study between computer scientists and fusion scientists in developing visual tools for studying patterns in flow fields from large-scale magnetic confinement fusion simulations. The authors visualize time-varying flow data by generating trajectory curves via massless particle advection and design a set of color functions, pathline filters, and projection methods specific to achieve fusion research objectives. These tools aid scientists in managing the visual complexity of large trajectory datasets and are crucial in locating and understanding subtle features of interest. The authors demonstrate the effectiveness of their techniques by using real fusion simulation data and provide insight by domain scientists. They also discuss how their methods address common scalability concerns.
KW - fusion simulations
KW - particle advection
KW - particle data
KW - scientific computing
KW - trajectories
KW - visualization
UR - http://www.scopus.com/inward/record.url?scp=84963826904&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963826904&partnerID=8YFLogxK
U2 - 10.1109/MCSE.2015.107
DO - 10.1109/MCSE.2015.107
M3 - Article
VL - 18
SP - 68
EP - 77
JO - Computing in Science and Engineering
JF - Computing in Science and Engineering
SN - 1521-9615
IS - 2
M1 - 7274254
ER -