activity
20002023
most citedDiffusion and spatial correlations in suspensions of swimming particles

231 citations · 1.9k across the 42 of their papers we have counts for

collaborators
Showing 2023 · physics.flu-dynShow all

6 papers · 2 filters

physics.flu-dyn2023

Effect of polymer additives on dynamics of water level in an open channel

Manish Kumar, Michael D. Graham

The presence of a tiny amount of polymers (a few parts per million) in a fluid dramatically reduces turbulent drag. For this reason, polymer additives have been proposed to be used…

physics.flu-dyn2023

Scattering-Informed Microstructure Prediction during Lagrangian Evolution (SIMPLE) -- A data-driven framework for modeling complex fluids in flow

Charles D. Young, Patrick T. Corona, Anukta Datta +2

An overarching challenge in rheology is to develop constitutive models for complex fluids for which we lack accurate first principles theory. A further challenge is that most exper…

physics.flu-dyn2023

Self-similar, spatially localized structures in turbulent pipe flow from a data-driven wavelet decomposition

Alex Guo, Daniel Floryan, Michael D. Graham

This study aims to extract and characterize structures in fully developed pipe flow at a friction Reynolds number of . To do so, we employ data-driven wavelet…

physics.flu-dyn2023★ 1 cited

Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning

Alec J. Linot, Kevin Zeng, Michael D. Graham

The high dimensionality and complex dynamics of turbulent flows remain an obstacle to the discovery and implementation of control strategies. Deep reinforcement learning (RL) is a…

physics.flu-dyn2023★ 1 cited

Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow

Alec J. Linot, Michael D. Graham

Because the Navier-Stokes equations are dissipative, the long-time dynamics of a flow in state space are expected to collapse onto a manifold whose dimension may be much lower than…

physics.flu-dyn2023

Predicting extreme events in a data-driven model of turbulent shear flow using an atlas of charts

Andrew J. Fox, C. Ricardo Constante-Amore, Michael D. Graham

Dynamical systems with extreme events are difficult to capture with data-driven modeling, due to the relative scarcity of data within extreme events compared to the typical dynamic…