CFD | Theory | Data Analysis | Experiments | Visualization | ML

Living fluids, like dense bacterial suspensions or mechanically actuated rods, can self-organize into flows with baffling properties. As the biological importance of these flows, from wound healing to morphogenesis, is becoming clearer, new technologies employing living engines are not distant either. However, living matter compels us, first, to rethink even viscosity and surface tension, and unravel the dynamics of emergent flows. Learning motility patterns of individual and swarm microswimmers further allows exploiting Nature informed navigational strategies. 

Now, how do we tame this emergence to our needs? Can we learn from across fields and start molding a new kind of matter?

CFD | Modeling | Data Analysis | Visualization

Polymeric fluids pose the challenge of accurately capturing the continuum effects of a dilute suspension of polymers, which behaves in a viscoelastic manner. These flows have parallels to Magnetohydrodynamics, as well as the modeling of bacterial motion, which makes it a particularly exciting problem that finds ready application in a range of industrial settings. A few questions are:

Can we improve the models to be closer to the underlying physics of polymers? What is the balance of "simple" and "good enough"? Do polymers behave similarly to the magnetic field in MHD, and are Elastic waves analogous to Alfvén waves? 

How do these flows transition to elastic turbulence at low Reynolds numbers, under different configurations, and what is the nature of instabilities? Pertinent is also the nature of high Reynolds number elasto-inertial turbulence, and its effect on the degradation of polymers over time reducing 

CFD | Theory | Data Analysis | Visualization

Although statistics has been the language for describing turbulence, it is structures that form our conceptual vocabulary and are key to uncovering flow organization. Are there really coherent structures at all scales? Can we identify and disentangle them from superposition? While wall induced patterns are well understood, what happens in the bulk? What are the structures associated with the underlying fractal skeleton of turbulence? Finally, we need to  translate these findings into more accurate turbulence models for LES/RANS, and for greater flow control in machinery like mixers and propulsion systems.

We shall tackle these ideas under three themes:

1. Identification-disentanglement of structures in fields using generalized correlations and Helmholtz-decomposition, aiming to uncover a generative hierarchy of coherence.

2. Singular structures and Intermittency in turbulence using local multifractality, uncovering ways to better understand anomalous dissipation.

3. Vortex-lines and reconnections, probing the very sinews of turbulence via the geometry of vortex knots and bundles.