# What do the differences between spatial and temporal spectra imply about the structure of turbulence?

In the study of turbulence (theoretically, computationally and experimentally), we often appeal to Taylor's Hypothesis, which (very loosely) summarized means taking measurements at one point in space throughout time is equivalent to taking measurements in space at the same instant in time. Experimentally, this is very desirable because it implies a single, stationary probe can capture the turbulent structure information.

But, all is not well with the hypothesis and this is known. It only holds when the primary advection mechanism is the mean flow, implying that the turbulent intensities must be small. It also is only valid in isotropic turbulence -- so wall-bounded flows are out (at least, when looking near the wall).

So the times that it is not valid, we can still measure a temporal variation at a point in space and we can (theoretically and numerically at least) still measure all of the spatial points at an instant in time. And from this, we can compute spectra and structure functions and everything else that we would normally compute to study turbulence. Only now, the frequency characteristics and the spatial characteristics are not equivalent.

What do we learn by measuring both that we didn't already know? If the hypothesis is invalid, we know the turbulence is homogeneous but not isotropic (and we likely know in what directions as well); we know whether the turbulent intensities are large or not. Is measuring both only helpful to show whether the hypothesis is valid or not? Does combining the information into a spatio-temporal spectrum/structure function/etc yield new insights?

So if I can measure both time and space, what does that really get me? Will something look non-turbulent in one but turbulent in another? Will structures appear in one that don't appear in the other? Will things like intermittency show up in one but not the other?

And if I can measure both, should I invest the time/money/effort? Or should I prefer one over the other?

• Well, this seems to be getting the usual treatment of deep questions outside the realm of theoretical particle physics or relativity. Which is a shame because it is interesting to me even though I don't do fluids. – dmckee --- ex-moderator kitten Oct 17 '15 at 18:45
• @dmckee Yeah... I didn't have very high hopes when I asked it, but I know that a community can't develop if we don't try to make it grow. It was inspired by this paper in which they go through considerable lengths to convert their experimental frequency spectra into spatial ones. We very often only record temporal data in our simulations also but we rarely try to look at the wavenumber characteristics. Unless it's a simple box simulation. – tpg2114 Oct 17 '15 at 18:49
• So I figured there should be a good explanation when to prefer one over the other. I might be able to figure out an answer by going through all the cited references in that paper. Maybe somebody already knows or can put the pieces together before I do though. – tpg2114 Oct 17 '15 at 18:50
• @dmckee Since relativity seems to be required to get attention, I actually had another idea for a question: physics.stackexchange.com/questions/213119/… – tpg2114 Oct 17 '15 at 19:33

## 2 Answers

Thanks for posing a great question. I would love for Taylor's hypothesis to be uniformly valid! It would mean my dissertation would be finished immediately. Let me take on your cluster of questions one-by-one, albeit out of order.

Will something look non-turbulent in one but turbulent in another?

Strictly speaking, no. There are advantages to spatial measurement, though, particularly when your flow is either bounded (jets, wakes, plumes), or exhibits coherent structures (high-Reynolds numbers). You might be familiar with the framework of model reduction or low-dimensional methods, for which Holmes et al. is the definitive text. Spatial measurement of turbulence that is not entirely stochastic and unbounded (or with periodic BCs) can lead to more optimal eigenfunction descriptions than Fourier modes.

Will structures appear in one that don't appear in the other?

See above: Yes, certainly! A single-point time record makes it hard to elucidate spatial flow structure, especially large-scale ones like that of VLSMs in turbulent boundary layers or the wave packets of turbulent jets. Much of the cutting-edge experimental work in turbulence is toward use of coherent spatio-temporal structures to form practical flow models.

Does combining the information into a spatio-temporal spectrum/structure function/etc yield new insights?

Yes! A major milestone for statistical turbulence models of inhomogeneous flows would be a wavenumber-frequency spectrum. This would fully represent the second-order turbulence statistics, which in most practical cases are what we're after. If you could model up to higher moments in space and time, even better. If you have a spare year or two to read it, I'd recommend Monin and Yaglom on this.

A case with which I'm familiar comes from the field of fluid-structure interaction. A turbulent boundary layer flowing over a deformable surface, say an aircraft hull, will produce displacements in the surface. Which structural modes are excited depends both on the spatial distribution of the forcing and the frequency content of the turbulence. You must have both a spatial and temporal description to even begin modeling this problem. Taylor's hypothesis does not work in this case, as you might imagine.

Will things like intermittency show up in one but not the other?

This is a tricky one. Strictly speaking, if a flow is intermittent, it is not stationary, and I somehow doubt you're interested in such flows (but I could be wrong). But, if one has a flow with spatial coherence, modulation of the turbulence by a very large structure could easily look like intermittency in the time domain, when in fact nothing has changed about the statistics of the flow at all. Spatially-distributed observations (or modeling) would help clarify this.

And if I can measure both, should I invest the time/money/effort? Or should I prefer one over the other?

This very much depends on your problem. Practically, (I sure hope) no one is going to block a publication if you are forced to use Taylor's hypothesis to estimate one-dimensional velocity wavenumber spectra, but you should certainly consider and report at which ranges the assumption is valid (usually large $|\mathbf{k}|$ relative to the integral scale). If you're working with static pressure in shear flow (as I am), then you may want to make the effort— Pressure sources in the velocity field are nonlocal, so Taylor's hypothesis is an even worse assumption.

Experimentally, spatial measurements are often extremely difficult. Farve et al. published a series of papers of space-time correlation measurements that took invention of a new apparatus and many years of experiments to complete. My current work is in atmospheric turbulence, and while some people are lucky enough to fund many tens of anemometer stations, studies with more than a handful of spatial positions are still rare.

I really can't speak to the computational side, except to say that I hope DNS papers with $\omega$-$\mathbf{k}$ spectra will become common soon. It seems to me that it would be easier computationally, but then constraints on grid spacing could pose serious hurdles. A recent paper by Wilczek et al. compares LES wavenumber-frequency spectra to a theoretical model they developed, and I'm keenly interested to see this work develop. The references should be a great resource, too.

• Looks like I have a lot of reading to catch up on! Interesting answer and welcome to the site -- it's always nice to bolster the fluid dynamics crowd here. – tpg2114 Mar 19 '16 at 20:51
• Thanks! Just remembered another recent paper, Wilczek and Narita. Wish I had time to read that work more closely. – Greg Lyons Mar 19 '16 at 21:18

Not sure it's what you aim at (maybe you should give more context about your studied fluid cases and your scales of interest), but since there are no other answers yet:

The geometry of your experiment surely impacts (polluting with structures) the spatial spectrum through several scales. The events in your experiment surely impacts (polluting with structures) the temporal spectrum (+ the time delay for the Kolmogorov cascading through scales). Intermittency and wakes probably triggers both here and there. So it might be that you get better "statistical" spectrum in one world or the other at a given place and time.