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Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

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Published on: July 12, 2018

Network community-based model reduction for vortical flows.

Muralikrishnan Gopalakrishnan Meena¹, Aditya G Nair¹, Kunihiko Taira¹

¹Department of Mechanical Engineering, Florida State University, Tallahassee, Florida 32310, USA.

Physical Review. E

|July 18, 2018

Summary

This summary is machine-generated.

This study introduces a network community approach for reduced-order modeling of complex fluid flows. The method effectively captures vortical interactions, enabling accurate prediction of aerodynamic forces.

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Preparation of Free-Surface Hyperbolic Water Vortices

Preparation of Free-Surface Hyperbolic Water Vortices

Published on: July 28, 2023

Area of Science:

Fluid Dynamics
Network Science
Computational Science

Background:

High-dimensional unsteady vortical flows present significant modeling challenges.
Understanding interactions among coherent structures is crucial for accurate flow prediction.
Existing methods often struggle with the complexity and dimensionality of these flows.

Purpose of the Study:

To develop a data-inspired, network-theoretic reduced-order model for vortical flows.
To identify and analyze key interactions within vortical communities.
To predict macroscopic flow dynamics and aerodynamic forces.

Main Methods:

Utilizing network-theoretic techniques to identify vortical communities based on dynamical behavior.
Distilling high-dimensional flow physics into vortical community centroids for dimensionality reduction.
Formulating reduced-order models for inter-community dynamics and force prediction.

Main Results:

Accurate capture of macroscopic dynamics for discrete point vortices.
Successful prediction of unsteady aerodynamic forces on a circular cylinder and an airfoil with a Gurney flap.
Demonstrated robustness against simulated experimental noise and turbulence.

Conclusions:

The network community-based reduced-order model effectively simplifies complex vortical flows.
The methodology provides accurate predictions of flow dynamics and aerodynamic forces.
The approach offers a robust framework for analyzing and modeling unsteady vortical flows.