
Dandelion Seeds Fly Using ‘Impossible’ Method Never Before Seen in Nature - bcOpus
https://www.nature.com/articles/d41586-018-07084-8
======
strainer

      """   """
      "" ^v^ ""
      "" ^v^ ""
      "" ^v^ ""  ... low pressure
      """..."""      held by vortex
      #########  ### dandelion falling   
      """""""""  """ rising air
    

I wonder about the chances of creating this kind of toroidal vortex above a
duct for lift. It seems necessary for the air to pass in the same direction as
the lift, yet lift is normally created by throwing air in the opposite
direction.

~~~
dleslie
Excuse me, I'm going to go launch SyncTerm now and browse some Mystic and
SynchroNet boards.

~~~
Angostura
I'm going to dig through the alt.fan.warlord archives

------
im3w1l
If you find this interesting I highly recommend you to look at some vape-trick
videos. Smoke/steam is great at visualizing vortices, and it's absolutely
astonishing how much aerodynamically control some people can achieve.

Creating lots of small vortices in rapid succession, merging vortices,
splitting vortices, letting vortices "suck in" other vortices.

[https://www.youtube.com/watch?v=Tmv228G8R4o&time_continue=1m](https://www.youtube.com/watch?v=Tmv228G8R4o&time_continue=1m)

------
seandavidfisher
> Previous studies have found that dandelion seeds always have between 90 and
> 110 bristles, says Nakayama

Those poor research assistants. Imagine counting hundreds of dandelion
bristles every day. Probably still not possible with AI/image recognition
either.

~~~
nl
That would 100% be possible with image recognition. Segment using UNet, then
measure the remaining joined pixels.

(Well thanks for the downvotes. I do this as my job, so I guess I'm doing the
impossible or something)

~~~
mdda
Perhaps people are wondering how your UNet is going to see the bristles on the
other side of the dandelion seed.

~~~
comex
Take photos from multiple angles?

~~~
TeMPOraL
Now you have a pretty ugly correspondence problem to solve.

~~~
titzer
I'm glad we have experts who can count to 100 instead. /s

------
aidos
That was a lovely little diversion! There’s a brief 1 minute clip in the
article that’s worth watching.

I do have one question though, how is the air flowing up through? It the seed
not falling for that to happen?

~~~
lordnacho
Yes, the seed is falling, but the vortex slows the fall enough for it to be
useful in distributing the seed as far as possible.

~~~
ehnto
Some seeds have a small leaf attached, making them spin as they fall and
slowing their descent for the same purpose. It's not about flying, just about
prolonging the time spent in a moving body of air.

~~~
pouetpouet
Sycamore and linden are the perfect example of this.

------
mar77i
Oh look, a vortex ring. Hermann von Helmholtz, while studying fluid dynamics
predicted that vortex rings had to exist.

[https://www.youtube.com/watch?v=Bcr9-93wXng](https://www.youtube.com/watch?v=Bcr9-93wXng)

------
mindfulhack
It's like nature's had millions of years to crunch through its own AI /
machine learning of evolution and adaptation, and we're about to unleash the
same thing on an impossibly faster scale. Imagine the technological
breakthroughs that are going to happen.

I'm optimistic.

~~~
dnautics
Genetic algorithms have been around in CS for a long time, in lots of highly
effective optimization problems, particularly in structural problems. They
don't seem to be as good for AI/ML problems (though the jury is still out in
hyperparameter type stuff). Gradient descent via backpropagation is quite
effective here, and that is very much _not_ like how the dandelion pod
evolved.

~~~
neuronic
Maybe that's as meta as it gets about evolution. The mechanisms of evolution
itself are put under selection pressure once we put alternatives out there.

Once we truly break the barrier between metal and meat, artificial processes
may create real life in completely unforeseen ways. Gives a new ring to
"asexual reproduction".

------
notatcomputer68
I always thought dandelion seeds were so open to reduce weight while still
catching a decent amount of the wind in its cross section, approximating the
flight of a dust particle.

Makes me wonder how much more of the wind a dandelion seed can catch with a
vortex compared to if it didn't form one.

------
zepearl
The result of natural/evolutionary selection?

Would be interesting to know if there are different types of dandelions (I
mean the part used to "fly") used by the same type of seed, each type
adapted/optimized for a particular climate (e.g. humid for asian areas, dry
for african areas, windy for coastal areas, ...).

~~~
adaptiveValleys
The criteria for adaptive evolution, in the classical Darwinian sense, are:

1). Reproduction. 2). Variation between the products of reproduction. 3).
Heritability between those variants. 4). Differential success among the
variants.

Anything that has those four characteristics will experience adaptive
evolution. Where it gets really fascinating is when you realize it applies to
things that don't go through biological reproduction, for example the
graphical user interface.

~~~
zepearl
Well, about the GUI: I'm not sure that that's following the rules; most try to
blindly follow trends, big companies tend to impose their GUIs, many implement
anything which is low-effort (therefore already made available by 3rd
parties), etc... => I don't see much selection/competitiveness here.

------
apo
Amazing that: (1) the mechanism involves a detached vortex; and (b) that it's
taken millennia to understand what makes dandelion seeds fly.

~~~
pbhjpbhj
Re (b) they appear to "fly" just like any other small piece of stuff, just
moving with the air, so I imagine it's more "there doesn't appear to be any
need to explain it".

They, dandelion seeds, appear to fly like other cotton-y plants do (eg heather
[1]). I wonder if they creates these vortices too?

I'd guess not as their structures aren't regular and they don't have the
weighted 'drop' to give stability?

[1] [https://oregonstate.edu/dept/nursery-
weeds/email_pubs/thistl...](https://oregonstate.edu/dept/nursery-
weeds/email_pubs/thistles/thistles.html)

------
taneq
This reminds me of the thing recently about spiders using electrostatic
tension in the air to generate lift with a streamer of silk.

Evolution is crazy good at finding and exploiting quirks in physics.

~~~
ironic_ali
I was going to mention this as I worked a couple of summers picking cherries
and every morning you'd be plowing through spider lines about head height
between the trees. There were lots of theories that got worse after a few
beers at the end of the work day, but it had puzzled me for a couple of
decades. Then learnt about spiders using the electromagnetic energy fields to
'fly'.

Nature is amazing.

------
miduil
I find this extremely fascinating, I've never put any efforts into thinking
how this is flying - just because it felt so natural and obvious. A great
example of that the obvious can hide greater details we still haven't
uncovered.

------
tines
> Those structures act like the wings of a bird or aeroplane, generating
> pressure differences above and below the wing to fly.

I thought we had settled this, airplane wings work by deflecting air downward,
not by the Bernoulli effect, right?

~~~
wahern
They're different ways to describe the same phenomenon. "[B]oth 'Bernoulli'
and 'Newton' are correct."
[https://www.grc.nasa.gov/www/k-12/airplane/bernnew.html](https://www.grc.nasa.gov/www/k-12/airplane/bernnew.html)

~~~
captain_perl
> They're different ways to describe the same phenomenon.

Well ... they're actually different ways to incorrectly describe the same
phenomenon.

Wind tunnels are still used for aircraft design because we can't accurately
model aerodynamics.

~~~
dj-wonk
Please clarify what you mean by ‘accurately’ ... without reducing this
discussion to a coarse dichotomy between ‘accurate’ or ‘not accurate’ —- which
would miss a main point of what models do and why they are useful.

My very rough understanding is that computer simulations of air flow are
sufficently accurate for a high percentage of predictions for many kinds of
objects. Fair? If not, under what cases does their accuracy suffer? Do we know
why?

I am interested in why wind tunnels are sometimes used. Possible reasons I see
are:

1\. building computer models of an object being tested is sufficently
difficult that it is more efficient to test in a wind tunnel

2\. computer simulations lose significant accuracy when it comes to certain
conditions ... but I don’t know what these conditions are

3\. human or policy issues, e.g. some people trust a wind tunnel result more
than a computer simulation.

~~~
btrettel
Fluid dynamicist here.

Short version: Scale models (like wind tunnels) are useful because the most
accurate simulations are extremely computationally expensive or
computationally intractable, and the faster less accurate simulations are
often so inaccurate that they are untrustworthy. Scale models are not 100%
trustworthy themselves, and to construct and use them you need to understand
similarity theory.

Long version:

The general field is called computational fluid dynamics (CFD for short).
There are broadly two types of turbulent computer simulations of flows: DNS
and not-DNS.

DNS stands for direct numerical simulation. These simulations are very
accurate, and sometimes are regarded as more trustworthy than experiments
because in a particular experiment you may not be able to set a variable
precisely, but you can always set variables precisely in a simulation.

Howver, in DNS you need to resolve all scales of the flow. Often this includes
the "Kolmogorov scale" where turbulent dissipation occurs. It could also
include even smaller scales like those involved in multiphase flows or
combustion. This is so extremely computationally expensive that it's
impractical (in terms of something you could run on a daily basis and iterate
on) for anything but toy problems like "homogeneous isotropic turbulence". In
terms of real world problems, DNS is limited to fairly simple geometries like
pipe flows. Those simulations will take weeks on the most powerful
supercomputers today. It's very rare for someone to attempt a DNS of a flow
with a more complex geometry, and I'd argue that such works are mostly a waste
of resources. Here's an interesting perspective on that:
[https://wjrider.wordpress.com/2015/12/25/the-unfortunate-
myt...](https://wjrider.wordpress.com/2015/12/25/the-unfortunate-myth-of-the-
hero-calculation/)

"Not-DNS" includes a variety of "turbulence modeling" approaches which
basically try to reduce the computational cost to something more manageable.
This can reduce the cost to hours or days on a single computer or cluster. The
two most popular turbulence modeling approaches are called RANS and LES.

Instead of solving the Navier-Stokes equations as is done in DNS, modified
versions of the Navier-Stokes equations are solved. If you time average the
equations instead, you'll get the Reynolds averaged Navier-Stokes (RANS)
equations: [https://en.wikipedia.org/wiki/Reynolds-
averaged_Navier%E2%80...](https://en.wikipedia.org/wiki/Reynolds-
averaged_Navier%E2%80%93Stokes_equations)

These equations are "unclosed" in the sense that they contain more unknowns
than equations. In principle, you could write a new equation for the unclosed
term (which is called the Reynolds stress in the RANS equations), but you'll
end up with even more unclosed terms. So, the unclosed terms are instead
modeled.

RANS is older, computationally cheaper, and usually computes the quantity that
you want (e.g., a time averaged quantity). LES is newer, and has better
justification in theory (e.g., good LES models converge to DNS if you make the
grid finer, but RANS will not), but it often doesn't compute precisely what
you want and the specifics of the LES models are often specified in
inconsistent ways. My experience is that people tend to ignore the problems
with LES or be ignorant of them. (Though I do believe LES is more
trustworthy.)

The problem is that modeling turbulence has proved to be rather difficult, and
none of these models work particularly well. Some are better than others, but
the more accurate ones typically are more computationally expensive.
Personally, I don't trust any turbulence model outside of its calibration
data.

Some people lately have proposed that machine learning could construct a
particularly accurate turbulence model, but that seems unlikely to me. People
said that same things about chaos theory and other buzzwords in the past, but
we're still waiting. Many turbulence models are fitted to a lot of data, and
they're still not particularly credible. Also, machine learning doesn't take
into account the governing equations. Methods which are similar to machine
learning but do take into account the governing equations are typically called
"model order reduction". If you want to do machine learning for turbulence,
you actually should do model order reduction for turbulence. Otherwise, you're
missing a big source of data: the governing equations themselves. (I could
write more on this topic, in particular about constraints you'd want the model
to fit which machine learning doesn't necessarily satisfy.)

Anyhow, scale models are basically treating the world as a computer. Often
testing at full scale is too expensive, particularly if you want to iterate.
"Similarity theory" gives a theoretical basis to scale models, so that you
know how to convert between the model and reality.

One of the most important results in similarity theory is the Buckingham Pi
Theorem:
[https://en.wikipedia.org/wiki/Buckingham_%CF%80_theorem](https://en.wikipedia.org/wiki/Buckingham_%CF%80_theorem)

This theorem shows that two systems governed by the same physics are "similar"
if they have the same dimensionless variables, even if the physical variables
differ greatly.

If any of this is confusing, I'd be happy to answer further questions.

~~~
dj-wonk
Wow, thanks for your well-written response. I didn't quite follow all the
details; in any case, I have a slightly better idea of what is going on. Next,
I look forward to learning a bit more about laminar versus turbulent flow.

I can relate to your comment: "Some people lately have proposed that machine
learning could construct a particularly accurate turbulence model, but that
seems unlikely to me". A healthy skepticism is important. Different inductive
biases in various machine learning algorithms will have a significant effect
here, I'd expect.

~~~
btrettel
Glad to help.

Here's some additional comments you or some other reader might find useful:

Dimensional homogeneity is the most important constraint I think most machine
learning folks would miss. It's not really an "inductive bias", rather
something which everyone agrees models need to satisfy, so it should be baked
in from the start. This is trivial to meet, actually; just make sure all of
the variables are dimensionless and it's automatically satisfied. (Depending
on the larger model, you might have to convert back to physical variables.)

[https://en.wikipedia.org/wiki/Dimensional_analysis#Dimension...](https://en.wikipedia.org/wiki/Dimensional_analysis#Dimensional_homogeneity)

In terms of "inductive biases", I'm not certain what that would entail in
terms of turbulence, but I'll think about it. Might be something to figure out
empirically.

Turbulence models which satisfy certain physical constraints are called
"realizable". Some of these constraints are seemingly trivial, but not
necessarily satisfied, like requiring that a standard deviation be greater
than zero. (Yes, some turbulence models might get that wrong!) The "Lumley
triangle" is a more advanced example of a physical constraint that a (RANS)
model needs to satisfy that often is not satisfied.

I'd be interested in applying machine learning type methods (combined with the
model order reduction approaches to include information from the Navier-Stokes
equations), but I'm not knowledgeable about them. My impression is that most
people applying machine learning to turbulence are novices at machine
learning. And I imagine most machine learning people applying machine learning
to turbulence are novices in turbulence and wouldn't know much anything about
the realizability constraints I mentioned.

Another issue worth mentioning is experimental design. I think the volume of
data needed to make a truly good turbulence model is probably several orders
of magnitude higher than anything done today for turbulence. Experimental
design could make this more efficient. I don't think most machine learning
people worry much about this. They seem to focus on problems which can be run
many times without much trouble. Acquiring data for turbulence is slow and
hard, so it's outside their typical experience.

------
namirez
Fascinating! For what it's worth, detached vortices are also critical to the
takeoff and landing of airplanes with delta wings, such as the Concorde.

------
sova
Does anybody have a link to a video of the lasers creating small vortices just
above the dandelion surface?

~~~
JoshMandel
[https://static-
content.springer.com/esm/art%3A10.1038%2Fs415...](https://static-
content.springer.com/esm/art%3A10.1038%2Fs41586-018-0604-2/MediaObjects/41586_2018_604_MOESM4_ESM.mp4)

[https://static-
content.springer.com/esm/art%3A10.1038%2Fs415...](https://static-
content.springer.com/esm/art%3A10.1038%2Fs41586-018-0604-2/MediaObjects/41586_2018_604_MOESM5_ESM.mp4)

[https://static-
content.springer.com/esm/art%3A10.1038%2Fs415...](https://static-
content.springer.com/esm/art%3A10.1038%2Fs41586-018-0604-2/MediaObjects/41586_2018_604_MOESM6_ESM.mp4)

[https://static-
content.springer.com/esm/art%3A10.1038%2Fs415...](https://static-
content.springer.com/esm/art%3A10.1038%2Fs41586-018-0604-2/MediaObjects/41586_2018_604_MOESM7_ESM.mp4)

[https://static-
content.springer.com/esm/art%3A10.1038%2Fs415...](https://static-
content.springer.com/esm/art%3A10.1038%2Fs41586-018-0604-2/MediaObjects/41586_2018_604_MOESM8_ESM.mp4)

~~~
chrisweekly
Thank you. That first video is just mesmerizing.

------
mattdemon
Wow, new parachute designs may be possible then! They will look scary, but can
be more effective.

~~~
namirez
Probably not! This phenomenon is not scale-independent. There is a parameter
called the Reynolds number (Re for short) which is the ratio of intertial
forces to viscous forces. For a dandelion seed Re is small which is the key to
the stability of the vortex.

For a parachute, the Re number is much higher which makes the dynamics of the
flow chaotic (called turbulence). There is a critical Re number beyond which
There is no way keep the vortex stable, or as they call it, the vortex bursts.

~~~
btrettel
You're most likely right, but there is at least one caveat which might be able
to help if we're lucky. (I'm a fluid dynamicist, though not an
aerodynamicist.)

The Reynolds number is only part of the picture. You also need a measure of
the strength of the turbulence. A common measure is the "turbulence
intensity", which you can think of as the standard deviation of the velocity
divided by the mean of the velocity. (Though that's only exactly true in
"isotropic turbulence".)

In certain circumstances you can compensate for a higher Reynolds number with
a lower turbulence intensity. The bristles of the dandelion may have a
turbulence reduction ability, so perhaps this is already being done. I'm not
certain how to reduce the turbulence level further as in this case it's mostly
an ambient property which is beyond the control of the dandelion. Some sort of
honeycomb structure upstream of the bristles might help, or it might hurt; it
depends on the details.

Here are some examples:

Pipe flow can remain laminar for higher Reynolds numbers if the turbulence
intensity is low enough. Though special turbulence control approaches (e.g.,
eliminating vibrations which could trigger transition to turbulence) laminar
pipe flows have been observed at a Reynolds numbers of about 100000, about 50
times higher than the typical Reynolds number where laminar flow ends.

Here's a quote from a review article:

[https://www.annualreviews.org/doi/abs/10.1146/annurev-
fluid-...](https://www.annualreviews.org/doi/abs/10.1146/annurev-
fluid-122109-160652)

> The impression gained from presenting data in this way is that there is a
> transition between two definable states. One is the relatively rare but
> well-defined state of motion, laminar flow, and the other is the more common
> and ill-defined state of turbulence. Experimental evidence suggests that the
> laminar state can be achieved in pipe flows over a wide range of Re with the
> record standing at Re = 100,000 by Pfenniger (1961). Reynolds himself
> managed to achieve Re = 13,000, and Ekman (1911) later improved on this to
> ∼50,000 using Reynolds’ original apparatus. [...] Achieving laminar flows at
> high values of Re is an indication of the quality of an experimental
> facility and gives some confidence that the observations will not be
> contaminated by extraneous background disturbances such as entrance flow
> effects, convection, and geometrical irregularities.

Matching the turbulence intensity of two wind tunnels is often necessary to
make the results comparable between the two wind tunnels. In the first volume
of Sidney Goldstein's "Modern Developments in Fluid Dynamics", there's a plot
showing (if I recall correctly) the Reynolds number at which the "drag crisis"
occurs as a function of turbulence intensity. This basically means that the
drag coefficient can be very sensitive to the turbulence intensity, at least
in special circumstances.

(Why I wrote this: In my dissertation, I have an entire section about how
turbulence intensity is too frequently neglected in analyses, particularly for
the problem I'm studying for my PhD.)

~~~
namirez
I agree with the importance of the freestream turbulence intensity, but at
high Re numbers, it's extremely hard to control it.

It can be shown mathematically, using a technique called parabolized stability
equations (PSE), that small disturbances amplify rapidly thorough non-linear
interactions in the frequency space. Hence, although it's possible to create a
laminar flow at high Re number in the lab, it's extremely hard to achieve in
nature.

One interesting case of this is the Rutan's Voyager airplane in the 80s. It
was designed to have a laminar flow over its wing to reduce drag. It worked
quite well until it faced rain drops at some point which messed up the
aerdynamics of the wing and caused the airplane to stall. At that point, they
had to add vortex generators on the wing to prevent the stall.

~~~
btrettel
Thanks for the interesting example. You're right that this is unlikely to
redeem a scaled up dandelion, but I thought it was still worth mentioning as
it's often overlooked.

I work in internal and multiphase flows, and changing the turbulence level is
much easier there than in aerodynamics.

I'll also have to look at the parabolized stability equations as I am not
familiar with them. If you have a preferred reference, I'd be interested.

------
kakarot
> Never Before Seen in Nature

> Many insects harbour such filter-like structures on their wings or legs,
> suggesting that the use of detached vortices for flight or swimming might be
> relatively common

I sense a bit of disconnect between the article and the headline.

~~~
taneq
Also:

> When some animals, aeroplanes or seeds fly, rings of circulating air called
> vortices form in contact with their wings or wing-like surfaces.

> Researchers thought that an unattached vortex would be too unstable to
> persist in nature.

I guess those animals and seeds aren't natural then?

~~~
mclehman
Aren't those two sections contrasting "in contact" with "unattached"?

~~~
taneq
Huh, looks like I misread that bit. Good catch!

------
paavoova
Perhaps ignorance on my part, but: why do they require highspeed cameras and
laser illumination to figure this out in 2018? Shouldn't physics by able to
model such a relatively simply structure and how air would move through and
around it?

~~~
coldtea
"Relatively simply structures" can be nigh impossible to model...

[https://en.wikipedia.org/wiki/Three-
body_problem](https://en.wikipedia.org/wiki/Three-body_problem)

------
jhabdas
Webpage not available

The webpage at
[https://www.nature.com/articles/d41586-018-07084-8](https://www.nature.com/articles/d41586-018-07084-8)
could not be loaded because:

net::ERR_TOO_MANY_REDIRECTS

------
cmurf
They aren't just good at flying. They're good at traveling on shoes, and
releasing on hikes, finding their way in national forests and wilderness where
they'd otherwise have had zero chance ending up.

------
jahbrewski
Is there a link to the actual journal article?

~~~
dpfu
There is, right in the article: Cummins, C. et al. Nature
[https://doi.org/10.1038/s41586-018-0604-2](https://doi.org/10.1038/s41586-018-0604-2)
(2018).

------
a012
I always thought dandelion seeds are like small parachutes those are slowly
falling down.

------
holografix
Could we design better parachutes using this tech?

------
pugworthy
Something something "Bumblebees shouldn't be able to fly" something something.

How is it we are shocked to find out we are sometimes wrong and not shocked
that sometimes we get it right?

My critique by the way is the headline, not the actual research. The headline
is clickbait IMO, and just as I suppose some don't like my comment (not well
thought out, emotional, etc.), the headline is the same.

E.g., consider, "Curious unexpected aerodynamic principles of dandelion seeds
lead scientists to new areas of discovery"

~~~
repiret
I agree its a bad headline. Maybe "Dandelion Seeds Fly Using Method Not
Previously Described in Nature", because: 1\. Of course its been _observed_
before. As the video points out, just about every kid has observed it. The
claim is really that nobody has previously understood the physics of it. 2\.
There's nothing impossible about it, clearly. Moreover, the article doesn't
claim that people had previously analyzed the seeds behavior and came to the
conclusion that it defies physics.

~~~
the8472
The vortex may not have been observed since air is mostly transparent.

~~~
whoopdedo
Or that no one had been able to answer, "What makes dandelion seeds fly?"
because no one had asked the question before. At least not asked it in a way
that prompted someone to look at a floating dandelion seed close enough to
notice the vortex.

------
dwighttk
I missed where they described how this was "Impossible"

~~~
macintux
"Researchers thought that an unattached vortex would be too unstable to
persist in nature."

~~~
dwighttk
thanks

