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    Area of Science:

    • Fluid Dynamics
    • Computational Physics
    • Particle Transport

    Background:

    • Vortex coreline detection is crucial in fluid dynamics.
    • Optimal vortex coreline detection relies on a reference frame moving with the vortex center.
    • Existing methods are limited, especially for inertial particles.

    Purpose of the Study:

    • To extend objective optimization frameworks for tracer particles to inertial particles.
    • To develop a robust method for detecting inertial vortex corelines in 2D and 3D flows.
    • To address the high-dimensional problem of optimal frame search for inertial particles.

    Main Methods:

    • Extension of objective optimization framework for tracer particles.
    • Reduction of 2D inertial vortex coreline detection to a critical point search.
    • Proposal of a recursive subdivision approach for 6D vector fields in 3D flows.

    Main Results:

    • Demonstration that 2D inertial vortex coreline detection can be simplified.
    • Development of an objective algorithm for 3D inertial vortex coreline detection using a 6D vector criterion.
    • Successful extraction of vortex corelines in various 2D and 3D vector fields.

    Conclusions:

    • The proposed method provides an objective approach to inertial vortex coreline detection.
    • The recursive subdivision algorithm effectively handles the 6D vector condition in 3D flows.
    • This technique enhances the study of fluid phenomena involving inertial particles.