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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!
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who make episodes like this possible.
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