A Combined Eulerian-Lagrangian Data Representation for Large-Scale Applications

The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations a...

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Vydáno v:IEEE transactions on visualization and computer graphics Ročník 23; číslo 10; s. 2248 - 2261
Hlavní autoři: Sauer, Franz, Jinrong Xie, Kwan-Liu Ma
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Analytical models
Computational modeling
Computer Science
Computer simulation
Correlation
Data models
data structures
Data visualization
Flow visualization
large-scale data
multi-resolution
Octrees
particle data
Representations
volume data
ISSN:1077-2626, 1941-0506
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Shrnutí:The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a "unit cell" based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer's needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved.
Bibliografie:ObjectType-Article-1
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SC0007443; SC0012610
USDOE Office of Science (SC)
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2016.2620975