[♪ INTRO] If you’ve ever been stuck in a traffic jam, you may feel like you’re in the middle of
a clogged pipe. And that intuition... isn’t too far off
from reality. Scientists can model traffic flow using equations originally invented for liquids in pipes. This is actually a common thing in science: Equations that were invented to describe one
physical system can end up being useful for something completely
different. In fact, fluid dynamics, the study of how
liquids and gases flow and evolve, is one area where this seems
to happen a lot. There are lots of scenarios where things that
are remarkably unlike liquids behave in pretty liquid-like
ways. Like birds. And… bitcoin. So by studying how liquids flow, we can learn
a lot about the rest of the world. Here are three examples. Crowds of humans can behave like fluids— and I’m not talking, like, in a stadium
where people are doing the wave. People act like fluids without even meaning
to. It mostly happens when a bunch of people get
really close together. For instance, one 2019 paper looked at people
lining up to start a marathon. Since there are tons of these races around
the world each year, and they look pretty similar, marathons are a great system to study. And at the start of each race, you tend to
see the same patterns. Like, at the start of a marathon, there’s
typically a column of thousands of people waiting to begin running. But since the street is only so wide, only
a few rows at a time are allowed to pass the start line. If you’ve seen a video of this happening, you can see what looks like a wave moving
back through the line of athletes every time some
runners are let through. And in the 2019 paper, researchers wanted to understand this pattern. Their study was the first to look at a crowd
of runners as a whole rather than as a collection of
individuals. They found that the apparent waves actually were waves of changing density and speed, so as they passed through the crowd, the number
of people in a given area fluctuated and then settled
back into an even density — almost exactly like sound waves moving through air particles. And the amazing thing was, this type of wave was predictable. While the crowd was in equilibrium, the density
of people was pretty consistent. It was actually pretty even from race to race,
too. But when waves did move through the crowd, they moved at constant speeds. Even from one race to another, one city to
another, the speeds of those density waves were similar
worldwide. Everything was so similar, in fact, that the
researchers were actually able to model the mass of people as a continuous fluid, and they could accurately predict the flow of runners. That’s right. Using physics equations that describe fluids, they were able to figure out how people would
flow— without knowing anything at all about what
the individual people were doing. Of course, marathon runners corralled at the
start of a race is a pretty contrived example of human crowds. But lots of research has been done on other, more erratic crowd movements, and they behave like fluids, too. For instance, one 2013 paper looked at people thrashing around in mosh pits at heavy metal
concerts. By analyzing videos, they found that moshers behaved remarkably like gas particles. So they could actually model the movement
of the moshers using equations normally used to study gases. Admittedly, this might not be the most useful
application of fluid dynamics in the real world — but
it is very cool. Still, research suggests that similar uses
of fluid dynamics could actually help us understand and prevent crowd behavior that becomes dangerous. Because, in tragic cases, erratic crowd movements turn into stampedes, like the fatal one at the Hajj in Saudi Arabia in 2015. Stampedes like this don’t happen because
of how individual people are acting. They happen because people in crowds are part
of a flow. Typical crowds look like what’s called laminar
flow of a liquid, where particles smoothly slip
past each other in clearly defined lanes. But sometimes, as crowds get too dense, small
perturbations, like someone tripping, can cause the flow to quickly become turbulent. In a turbulent flow, movement becomes chaotic and hard to predict, and people are pushed in essentially random directions. Situations like this can become more likely if crowds are forced through bottlenecks, like narrow emergency exits. But there is a bright side: By treating crowd flows as fluids, we can
use fluid dynamics to lower the odds of stampedes happening and make crowds flow more smoothly. For instance, simple measures, like adding columns or other obstacles near
emergency exits, might actually speed up evacuations by reducing the number of directions people approach from. This is a technique also used for fluids,
so even though people aren’t actually water molecules,
it turns out they can sometimes behave in pretty similar
ways! Now, a lot of individual species can act like
fluids at times —not just humans, but also birds in flocks
or fish in schools. And the language of fluid dynamics can be
useful for describing how they move, too. But it can also be useful for describing how
species as a whole move across landscapes. In 2018, some researchers wrote a paper doing
exactly that. Their goal was to understand how species respond to changes in their environment — especially human-made changes like deforestation or habitat fragmentation. Naturally, in a given landscape, species of
animals and plants spread out and populate different places. So, first, the team wanted to understand how quickly different species spread naturally. They created a model using the equations that describe how a fluid moves through a porous material, like a sponge. In fluid flow, the viscosity of a fluid tells
you how resistant it is to flow. And species have an analogous property, called
mobility, which measures how readily they disperse. Like, you wouldn’t expect rabbits to spread
out over a landscape at the same speed that, say, lichen does—their mobility is different. Then there’s permeability, which describes how readily a material lets fluids move through
it. So the researchers’ model works out how
permeable a landscape is to different species that are essentially “flowing” through it. Using this model, they simulated a species
spreading out across a landscape from west to east. Then they tested how different factors — like
the mobility of the species and the permeability of the
terrain — influence the rate of that spread. And what’s nice about this model is that
it can be used to test how species react to changes in their
environments. So we can use it to model what happens if,
say, the area becomes more urban and built-up. And that can help us figure out how much humans are interfering with species’ habitats. We can also use this model to work out how
to keep a population of a species connected when human activity disrupts a landscape. It’s not a perfect analogy, because the
spread of species doesn’t work exactly like a fluid. For example, in fluids, permeability usually only depends on the material itself, nd not the fluid going through it. But in this model, permeability depends pretty
strongly on the species, since a given terrain may
be much easier for a species of birds to spread
through than a species of trees. So there are some limitations, but overall, fluid dynamics give us a super useful way to look at a species as a whole. Finally, our third weird thing that acts like
a fluid isn’t even in the physical world at all. We’re going to get digital and look at what
in the world cryptography has to do with fluid dynamics. Cryptography is the science of sending information
securely —think “secret codes” and “cyphers.” And the key to modern, online cryptography is something called hashing, which is important for everything from entering passwords to paying people with bitcoin. Basically, when you enter a password on a
website, you want to make sure no one who hacks the website can get your password. So any good site will use something called a cryptographic hash function to convert your
password into what’s called a hashed form—that’s
this weird gibberish that only the computer can understand. These functions typically use super advanced
math, but the basic concept isn’t too tricky. Overall, a hash function just needs to have three properties to be useful. First, it needs to be unique, meaning that
you can never get the same string of gibberish from two different passwords. Second, it needs to be repeatable, meaning
that any time you apply that function to the same
password, it will produce the same gibberish. And finally, it needs to be one-way, meaning
that the process that turns it into gibberish is easy to do, but really, really hard to undo —like trying to flawlessly un-break a mirror. If the hash function can do these three things, the website never needs to store your password. Instead, every time you enter the password, it will just apply that function to make gibberish from whatever you entered. Then it’ll compare that to the password-gibberish that it stored when you made the password to see if those two gibberishes match. So, what’s all this got to do with fluid
dynamics? Right. So, in 2018, a scientist at Stanford figured
out that the equations of fluid dynamics can behave like a hash function. Which is a really abstract idea, but let’s
look at an example that’s much more familiar: a cup of coffee. Think about what happens when you pour milk
into coffee and stir it. At first, the milk is a white drop in the
coffee, but if you stir it, the mix of coffee and milk gets these weird,
random patterns. Intuitively, you know that you’ll never
be able to recreate those exact same patterns in a fresh cup of
coffee. But according to fluid dynamics equations, it’s not technically impossible. If you drop the exact same amount of milk in the exact same amount of coffee, at the exact same temperature and pressure, and stir it the exact same amount in the exact
same direction, with exactly the same all of everything, then you will get the same pattern as before. These are the initial conditions of the process. And with the same initial conditions, the
process is repeatable. It’s incredibly unlikely, and even tiny
changes in the initial conditions can completely mess
it up, but it is possible. The Stanford scientist realized this and made
two more intuitive leaps. He knew that it’s much easier to create
a specific pattern given the initial conditions than it is guess
the initial conditions by looking at the final pattern. In other words, the process was one-way. And he worked out that a particular pattern
of milk and coffee can only come from one exact set of initial
conditions. So the stirring process was unique. And if it was repeatable, one-way, and unique, that meant the process of stirring milk into
coffee had all the properties of a good hash function. Essentially, the initial conditions are like
the password, the equations are like the hash function, and the pattern produced is like the gibberish
the website stores. So now we know the complicated situations in cryptography aren’t just some weird digital
thing. It crops up in the real world, too. And knowing that can help us think of creative new ways to study the properties of hash functions and think about cybersecurity. Which is super important, because cryptography is always an arms race between researchers
and hackers, so any potential new source of hash functions
is always useful. So yes, the physics of coffee could potentially
help us improve bitcoin. More broadly speaking, making analogies like the ones we’ve looked at here is a really important part of doing science. It allows you to make connections you’d
never otherwise think of and come up with innovative ideas to solve problems. And there are tons of other systems out there that look like fluids, too, from electrons
flowing in currents to galaxies flowing in space. It turns out that lots of things in our universe like to go with the flow. Thanks for watching this episode of SciShow! And a special thanks to our patrons on Patreon, who make episodes like this possible. It takes a lot of people to make a SciShow
video, and we couldn’t do it without you. If you’re interested in learning about ways
to support us, you can find out more at patreon.com/SciShow. [♪ OUTRO]
