Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Christopher J. Keylock, Maurizio Carbone

TL;DR
This paper introduces a network-based approach to classify and analyze the Lagrangian dynamics of the velocity gradient tensor in turbulence, revealing the importance of non-normality and complex terms in VGT behavior.
Contribution
It develops a novel network participation method for classifying VGT states, incorporating non-normality and complex dynamics for improved turbulence analysis.
Findings
Network representation of VGT is more compact than joint invariant distributions.
Including non-normality improves VGT state classification accuracy.
Community-based classification aligns with traditional invariants and reveals complex flow structures.
Abstract
Complex network analysis methods have been widely applied to nonlinear systems, but applications within fluid mechanics are relatively few. In this paper, we use a network for the Lagrangian dynamics of the velocity gradient tensor (VGT), where each node is a flow state, and the probability of transitioning between states follows from a direct numerical simulation of statistically steady and isotropic turbulence. The network representation of the VGT dynamics is much more compact than the continuous, joint distribution of a set of invariants for the tensor. We focus on choosing optimal variables to discretize and classify the VGT states. To this end, we test several classifications based on topology and various properties of the background flow coherent structures. We do this using the notion of "community" or "module", namely clusters of nodes that are optimally distinct while also…
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Taxonomy
TopicsComputational Physics and Python Applications · Fluid Dynamics and Turbulent Flows · Opinion Dynamics and Social Influence
