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Dandelion Seeds Fly Using ‘Impossible’ Method Never Before Seen in Nature (nature.com)
395 points by bcOpus 6 months ago | hide | past | web | favorite | 108 comments



  """   """
  "" ^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.


I had to read your comment twice before I realized it's not, in fact, a short poem.

[Edit] Maybe it is after all. :)


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


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


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


[flagged]


Please don't do this here.


> 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.


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)


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


Think you are misunderstanding what they are counting. The bristles of the seed sit on a stalk and are quite visible individually for the most part, because they are quite thin and stick out in different directions. If you were to place it so that the stalk points toward you, like in the Nature video, the bristles would stand out really clearly.

https://commons.wikimedia.org/wiki/File:Dandelion_seed_-_May...


Take photos from multiple angles?


And how do you deduplicate the ones that are visible on multiple angles?


Now you have a pretty ugly correspondence problem to solve.


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


Squash it flat.


I think the real question is, which is more cost/time efficient:

a) Hire someone or a company skilled enough to whip up some classifier that can count these from some accuracy, level, which entails paying a developer, and probably 5-10 graduate students to sit around and count dandelion bristles on a good number of dandelion seeds to create a training set.

or

b) Just have 5-10 graduate students sit around and count dandelion bristles on a good number of dandelion seeds

Given that a is a super set of b, and the extra portions of a are likely much more expensive than all of b, I think the answer is fairly clear...


Hm. Are NNs reliable enough to be sure you're getting a correct result?

I'd imagine this kind of task would lend itself to some old-school image recognition techniques - photograph against uniform background, threshold, mask the middle part and count contiguous regions.


ffs not everything needs ML. It's not like they need to scale to every individual dandelion in the known universe.


The bristles seem long enough. You could cut them, separate them, take a well contrasted photo, repeat. The image recognition task would be pretty easy at that point I think as you're just counting separated bristles.


That's kind of at the point where the solution takes so long to develop that you might as well count them manually.


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?


It flys in the same way a glider flys. A glider generates lift over the wings, but the lift comes from the airflow, and you get the airflow by trading potential energy for kinetic energy. Thus the glider is always descending through the airmass, albeit at a very shallow glide angle. But the airmass itself isn't stationary - the sun heats the ground unevenly, so some air above it is warmer and rises, and some parts are colder and sink, with winds circulating between them. A glider can stay airborne for hours if the pilot is good at finding rising air, and circling within it. So long as the air is rising faster than the glider is descending within the airmass, the glider will gain altitude.

The dandelion is doing the same thing. It's creating lift by descending slowly within the airmass, though the process of creating lift is via the vortex ring, rather than airflow over wings. Its aim is to fall very very slowly. Then it will often be falling slower than the airmass is rising, and it will be carried up and far away. Of course there's no pilot, so it can't actively seek out rising air, but there are an awful lot of dandelion seeds, and only a few need to get lucky to spread a long way.


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.


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.


Sycamore and linden are the perfect example of this.


My intuitive understanding is that the vortex is a consequence of the slower flow through and not the other way around.


Potential energy from gravity goes to kinetic energy of stable vortex ring rather than kinetic energy of falling down.


I imagine they get blown with the wind, start falling, which triggers the formation of the vortex - and that vortex is somewhat self-sustaining, almost completely arresting the fall.


Wind can create upward movements of air near the ground.


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


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.


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.


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".


>Imagine the technological breakthroughs that are going to happen.

Or the horrific, ending-of-human-race potential for mistakes (or purposeful harmful action for profit/terrorism/state actions/etc).

I'm realistic.


I'll explain: it's a rare and perhaps AI-religious view, but my optimism isn't so much for the human race, but for the flourishing of information itself. If I were an ant, and were aware of humans' superiority over me, I'd be glad to see them succeed.


Are we re-discovering nature as a big not-artificial intelligence? like that big entity someone will start to respect, venerate and serve as a mythical goddess?


A syncretization of technology and scientific thought, hearkening back to our more animistic tendencies, sounds both poetic and plausible.


I don't think worship is an "animistic" quality. I think the tendency to worship is very much a refined trait in highly intelligent organisms.


You're right in the sense that ritualised, repeated behaviour performed for no obvious gain is something very unusual. Most animals don't waste energy on things that don't have a material impact on their life.

I'd disagree with your point about intelligence though. It's simply that we have more resources than we need. Even the stupidest animals would develop rituals if they could afford to.


Animals like rats and pigeons actually do develop superstitious rituals.

Skinner documented it in 1948, and there's a fair bit of literature on it.

https://psychclassics.yorku.ca/Skinner/Pigeon/


You can't say that ritual and worship doesn't have material impact. You have no way to prove or disprove that.


Sorry, animistic here did not reference the fact that we are animals, rather that we used to worship them and other entities we saw in nature, like the sun, and that we might come to view computers/machines as also having a spiritual essence.


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.


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, ...).


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.


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.


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


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...


Relevant xkcd: https://xkcd.com/1867/


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.


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.


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.


> 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?


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


> 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.


They're both correct in that both need to be true for lift to occur. If there is a force upwards on the wing then there needs to be an opposing force, and this necessarily must come from a difference in air pressure between the top and the bottom of the wing. Also, for the force upwards to exist, there must be an acceleration of the air around the wing downwards. It's also true that for a pressure difference between the top and bottom of the wing to exist, the air must accelerate downwards and vice-versa. Similarly, the low-pressure air on the top of the wing must be going faster, and faster air must be lower pressure (it is, however, not true that the air travelling over the top of the wing must take the same time to pass the wing and the air travelling over the bottom. This is in fact usually false).

These are all fairly straightforward consequences of the basic rules which air must follow when flowing. The wing produces lift because the shape of it means that all the consequences of the above are the only ones which follow all the rules, much like a sudoku or crossword. The details of actually solving the full puzzle (in way which tells you how much lift and drag you get) turn out to be fiendishly complex, but the basic reasoning is not too hard to understand, apart from the fact that people get confused by the fact that you can explain it 'simply' in multiple different ways by focusing on only one part of the whole crossword.


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.


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...

"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...

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

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.


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.


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...

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.



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.


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



Thank you. That first video is just mesmerizing.


Click through to the paper and you can view the figures and supplementary information for free. Protip: while it's frustrating to find a paywalled paper if you just want to know how it works the supplementary materials are usually unrestricted, better written, and easier to understand than the paper itself.


Thanks ! That is a good pro tip. Did not think about supplemental papers and resources.


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


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.


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-...

> 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.)


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.


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.


The HondaJet was also designed to take advantage of superlaminar flow over the wings and body. It's why the turbines are mounted on pods held high above the wings, instead of closely slung underneath like a traditional design.


That was super interesting. Thank you for commenting!


Could it work at higher speeds, like a craft in re-entry from space? Like a dandelion pre-chute.


I don't know much about high speed flows, but I imagine the answer is no.

While the gas temperatures would be higher due to viscous heating, which would increase the viscosity of the air, the increase in viscosity is much lower than the increase in velocity. So the Reynolds number would still increase. I very strongly doubt the turbulence intensity would be lower in this case too.

Plus, I imagine the bristles would be quickly ablated away.


That gave me an idea for the bristles being fritzy ends of cables, slowly being wound out and sacrificed to the wind. Like an ablative shield.

Not saying that it would be good for anything, it just produced a cool image in my head. :-)


Perhaps many small dandelion apertures could be used in a larger chute (not necessarily the whole surface), rather than trying to make a single large seed-chute.

Perhaps I should have tried to patent that ...


Ehhh, I imagine the seed/filament weight ratio is much closer than the human/filament weight ratio... if you blow this up to human size/weight proportions the filaments would likely need to be gigantic.


I don't think you can scale the filaments up. Scaling this up would mean adding small holes to a parachute and filaments around those... And then we would get into the task of folding something full of small filaments.


> 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.


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?


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


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


What makes a vortex unattached?


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?


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

https://en.wikipedia.org/wiki/Three-body_problem


Fluid dynamics is hard and a simulation can only work with the inputs and parameters you set it up with.


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.


Is there a link to the actual journal article?


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



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


Could we design better parachutes using this tech?


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"


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.


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


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.


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

You can even shorten that to "unexpected aerodynamic principles of dandelion seeds".


Complaints about headline bias/clickbaityness or suggesting better links seem to comprise around half of HN comments.


Why is it clickbait? Do you mean the title is exaggerated? But "impossible" is still in quotes. It's not clickbait.


I missed where they described how this was "Impossible"


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


thanks


"Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize."

https://news.ycombinator.com/newsguidelines.html


Well, obviously not literally impossible, more like incredible. They float away because they form an unattached air vortex above themselves, and it sucks them up. Flight without work.


"Dandelion seeds fly using a method that researchers thought couldn’t work in the real world [...] Researchers thought that an unattached vortex would be too unstable to persist in nature."


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

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