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Viscoelastic Turbulence

 A viscoelastic fluid is created when long-chain polymers are added to a Newtonian solvent which gives the fluid elasticity. Fascinating new behaviour is then possible including the possibility of elastic turbulence at vanishingly small Reynolds numbers. Recently another form of turbulence - Elasto-Inertial Turbulence - has been found experimentally and numerically in the FENE-P model. Our work (in collaboration with Yves Dubief, Univ. Vermont) is directed at trying to understand the origins of this new form of turbulence. Possible applications include a better understanding of drag reduction in shear flows ( a few parts per million of polymer in water can reduce the drag on a wall by up to 80%), of microfluidic flows (e.g. curved channels are used to trigger turbulence in viscoelastic fluids so as to efficiently cool computer chips) and drag reducing therapies for decreasing atherosclerosis.

 


Exact Coherent Structures (ECS) in Turbulence 

[a finite baffle]

 

Coherent structures are large-scale structures seen fleetingly but repeatedly in turbulent flows. The `exact' part comes from a more recent realisation that these coherent structures are typically exact solutions of the Navier-Stokes equations with simple time behaviour which are `nearly' stable (meaning they are unstable but with only a small number of unstable directions compared to the huge number of stable directions). Viewing turbulence has a very high dimensional dynamical system, these ECS are visited transiently in phase space. ECS are equilibria, travelling waves, periodic orbits and possibly tori. One attractive but challenging approach to developing a predictive theory for turbulence  is to produce an expansion of the turbulence in terms of these ECS so that a given turbulent property is a weighted average of that property for each ECS visited (e.g. Chandler & Kerswell, J. Fluid Mech. 2013, Lucas et al. J. Fluid Mech. 2017).  A variety of projects are active in this area with perhaps the most notable being the use of machine learning to help identify periodic orbits buried in the turbulent attractor (with Michael Brenner, Harvard Univ.) and developing multiscale solutions to the quasilinearized Navier-Stokes equations (with Greg Chini, Univ. New Hampshire).

 

 


Exponential Asymptotics

The Navier Stokes equations at high Reynolds number can be considered as a singular perturbation of the Euler equations, and the asymptotic structure of solutions to such problems often requires consideration of terms which are exponentially small in the perturbation parameter(s). We are studying the exponential asymptotics of the Kuramoto-Sivashinsky equation, which can hopefully be used as a toy model for analysing similar problems in fluid dynamics  (Chris Sear and Stephen Cowley)

 

Koopman Analysis & DMD

[Noise generated by turbulent flow over an owl's wing]

 

Koopman analysis holds out the possibility of representating measures or observables of nonlinear dynamics using expansions based upon Koopman eigenfunctions which evolve exponentially in time. The discovery of dynamic mode decomposition (DMD) in 2010  was quickly realised to provide a practical way to identify Koopman modes from data (either from experiments or simulations). While the use of DMD to extract the dominant frequencies in a data set is largely accepted, the usefulness of thinking in terms of Koopman modes and eigenfunctions is far less clear. Our work here has been focussing on exploring Koopman analysis in simple systems and the Navier-Stokes equations (e.g. Page & Kerswell, J. Fluid Mech. 2019, 2020).

 

 

 

Rotating Fluids & Wave Turbulence                                            

 

 

Many planetary objects rotate rapidly and  experience persistent perturbations so that the flow is not uniform rotation (e.g precessional and/or tidal forces cause distortions of the rotating flow). If this distortion is large enough the flow can become unstable and ultimately a turbulent state can arise. Appreciating exactly what flow response is generated is  important for understanding many geophysical and astrophysical processes (e.g. the geodynamo and the subterranean ocean on Enceladus - Wilson & Kerswell, EPSL, 2018). The initial instability can be understood as the excitation of normal modes - variously called inertial waves or Poincare modes - on top of uniform rotation. Once they reach finite but still small amplitude, a weak wave turbulence approximation is then applicable. Current work is looking at how to use this approximation to make predictions about the flow response. 

 

 

Variational Methods in Turbulence

 

Variational methods provide a unique approach to derive rigorous inequality information on turbulence in the form of bounds on key global properties such as heat flux in convection, momentum flux in shear flow or mass flux in a pressured-driven flow. The hope is that these bounds at least capture the correct asymptotic scaling with the parameter(s) of the problem (e.g. the heat flux with the Rayleigh number scaling in Boussinesq convection as in figure) and at best also produce relevant flow fields. Recently we have been exploring efficient ways to solve the Euler-Lagrange equations which emerge from these problems (e.g. Wen et al. Phys. Rev. E  2015) and pushing known techniques such as the Background method to the limits (e.g. Ding & Kerswell J. Fluid Mech. 2020). One goal of this approach is to establish a bridge between actual solutions of the underlying governing equations and a variational problem which maximises some key quantities subject to just a subset of the dynamical constraints.

 

 

Destabilizing Turbulence

 

A turbulent flow exerts a far greater drag on boundary walls than a laminar flow which consequently can lead to far higher energy costs in transporting the same amount of  mass (e.g. oil in pipelines). There is therefore a huge interest in being able to eliminate or destabilize turbulence to get back the underlying laminar flow. Recent experiments in pipe flow suggest that flattening the mean flow profile is an effective way to accomplish this. We (with Elena Marensi & Ashley Willis, Univ. Sheffield) have been studying this observation using optimisation techniques (e.g. Marensi et al. J. Fluid Mech. 2019, 2020). The focus of our work so far has been to optimally design a baffle using a fixed amount of material which can relaminarise turbulence at the highest possible flow rate (or Reynolds number).