
Fluid mechanics considers two frames of reference for an observer watching a flow field: Eulerian and Lagrangian. The former is the frame of reference traditionally used for flow analysis, and involves extracting particle trajectories based on a vector field. With this work, we explore the opportunities that arise when considering these trajectories from the Lagrangian frame of reference. Specifically, we consider a form where flows are extracted in situ and then used for subsequent post hoc analysis. We believe this alternate, Lagrangian-based form will be increasingly useful, because the Eulerian frame of reference is sensitive to temporal frequency, and architectural trends are causing temporal frequency to drop rapidly on modern supercomputers. We support our viewpoint by running a series of experiments, which demonstrate the Lagrangian form can be more accurate, require less I/O, and be faster when compared to traditional advection.
high-performance computing, pathline interpolation, flow visualization, particle advection, compression
high-performance computing, pathline interpolation, flow visualization, particle advection, compression
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