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

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    Area of Science:

    • Computational Science
    • Data Visualization
    • Scientific Simulation

    Background:

    • Eulerian and Lagrangian reference frames offer distinct views of scientific systems.
    • Large-scale simulations often generate data in both formats, increasing analysis complexity.
    • Correlating Eulerian and Lagrangian data is computationally challenging, especially at scale.

    Purpose of the Study:

    • To develop a novel data representation combining Eulerian and Lagrangian frames.
    • To enable efficient simultaneous analysis of data from both reference frames.
    • To facilitate large-scale scientific data exploration and visualization.

    Main Methods:

    • A joint Eulerian-Lagrangian data format is proposed.
    • Lagrangian data is reorganized into a 'unit cell' approach based on the Eulerian grid.
    • The method supports efficient out-of-core sampling, querying, and multi-resolution data subsetting.
    • A fast flow-aware trajectory construction scheme is developed.

    Main Results:

    • The joint format allows simultaneous efficient operation with both Eulerian and Lagrangian data.
    • The 'unit cell' approach enables out-of-core processing for large datasets.
    • Multi-resolution subsets cater to viewer needs, and trajectory construction is accelerated.
    • Demonstrated effectiveness on three large-scale real-world scientific datasets.

    Conclusions:

    • The joint Eulerian-Lagrangian representation significantly enhances the analysis of large-scale scientific simulations.
    • This approach overcomes computational difficulties in correlating disparate data formats.
    • The method offers substantial performance gains and improved data accessibility for scientific exploration.