
arXiv: 1610.00197
We present a frame-invariant method for detecting coherent structures from Lagrangian flow trajectories that can be sparse in number, as is the case in many fluid mechanics applications of practical interest. The method, based on principles used in graph colouring and spectral graph drawing algorithms, examines a measure of the kinematic dissimilarity of all pairs of fluid trajectories, measured either experimentally, e.g. using particle tracking velocimetry, or numerically, by advecting fluid particles in the Eulerian velocity field. Coherence is assigned to groups of particles whose kinematics remain similar throughout the time interval for which trajectory data are available, regardless of their physical proximity to one another. Through the use of several analytical and experimental validation cases, this algorithm is shown to robustly detect coherent structures using significantly less flow data than are required by existing spectral graph theory methods.
FOS: Computer and information sciences, Applications of graph theory, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, low-dimensional models, Machine Learning (stat.ML), Physics - Fluid Dynamics, Dynamical Systems (math.DS), 530, Vortex methods applied to problems in fluid mechanics, Statistics - Machine Learning, FOS: Mathematics, general fluid mechanics, nonlinear dynamical systems, Mathematics - Dynamical Systems
FOS: Computer and information sciences, Applications of graph theory, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, low-dimensional models, Machine Learning (stat.ML), Physics - Fluid Dynamics, Dynamical Systems (math.DS), 530, Vortex methods applied to problems in fluid mechanics, Statistics - Machine Learning, FOS: Mathematics, general fluid mechanics, nonlinear dynamical systems, Mathematics - Dynamical Systems
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