(jazzy music)
- How many lives could you save
by opening the right window,
and at the same time closing the wrong windows?
That's a question that Emmanuel Vanoli thinks about a lot.
When his grandmother contracted coronavirus,
she had to go into hospital,
and Emmanuel started to think about ways
that he could help that hospital
to reduce the spread of coronavirus.
And Emmanuel Vanoli has a very particular set of skills,
skills that he acquired over a long career.
Skills that make him a nightmare for viruses like corona.
That's my Liam Neeson impression.
It's actually very good.
It's a good,
if the virus had let his grandmother go
that would have been the end of it.
I can't keep up with that, stupid.
The point is, Emmanuel is really good
at modeling the flow of fluids.
Unlikely Liam Neeson, Emmanuel doesn't work alone,
he works for a company called Dassault Systèmes.
And I should say at this point that
Dassault Systèmes are paying for this video.
You know, I only make videos
about things that I find interesting.
So I would have made this video anyway,
but it's important to be transparent about these things.
- My grandmother, she's a 88 years old,
and she got infected by the COVID-19,
and I contacted the directors of the hospital
and tried to see with them if I can do anything.
So we started to model the hospital.
- The starting point for Emmanuel
in this case was actually a PDF
of the floor plan of the hospital.
And from that you can create a 3D model.
But there's so much more to consider,
obviously windows and doors and ventilation,
but also any sources of heat create convection currents,
so people, computers, radiators.
It's actually incredibly complicated,
but once you have that satisfactory 3D model,
what do you do next?
- I put fake patients in each rooms,
and then I make them cough.
So, in each- - I saw the animation.
It's great.
It's like a little mushroom cloud, right?
That's the one coughing, is it?
Okay.
When Emmanuel ran his simulation
on the current layout of the hospital,
he discovered something really unfortunate.
You've got this area of coronavirus patients,
and on the same floor and area for other patients.
And according to his model,
air was flowing easily from the coronavirus area
to the non-coronavirus area.
So the task for Dassaut Systemes was then
to find a solution for the hospital,
and not one that requires knocking down walls
or building new walls.
- The easiest one was to open windows
in strategic position
and create some additional airflow
that will block the propagation of the virus.
First one was in the middle
of the biggest corridor of the floor,
to really cut the airflow in two ways
and block a part of the virus.
And the second one was in between
the COVID area and the safe one.
And since the hospital was in a low pressure situation
compared to the external air,
because you only have extraction,
you are sucking fresh air from the outside,
and then thanks to that you can block
most of the contamination or the contaminated air
in between the two areas.
And this is basically the solution that
the management of the hospital decided to follow.
- Isn't that a great outcome?
- Dassault Systemes have worked
with many hospitals now to give them similar insights.
And I suppose like, this is the point of modeling.
With something like airflow, it's chaotic,
it's hard to predict.
You can't really figure out what it's gonna do
just by looking at it.
Like you might think to yourself,
well if I open this window
and it'll flow this way and this'll happen.
But it doesn't work like that.
You have to do the modeling.
You have to look at,
these are the things that the hospital can vary,
these different windows, doors, and so on,
let's model every possible combination of those things
and see what happens.
And you discover through doing that
that actually it's this window and this window
that changes the airflow in the way you need it to.
- After seeing the simulation results,
you will say, oh, yes, yeah, it's obvious, yeah, of course.
In fact, nobody saw that before.
So that's why it's powerful
and very easy to understand and share.
- I wanted to get to the point where
I really understood how fluid flow is modeled,
computational fluid dynamics.
Like not to the point of being an expert,
but just to the point where
I have a really satisfying understanding of what it is.
I mean, that's the basis of most of my videos really.
But with modeling fluid flow,
it turns out that actually the way it's done
is verging on bizarre.
There are many ways to model the motion of fluids,
and the ones that Dassault Systemes uses
are the popular ones,
the ones that make sense for that particular problem.
So I'll talk about those.
You're actually doing two things.
The first is you're tracking the motion of particles.
I'm not talking about the atoms and molecules
that make up the air.
I mean like when someone breathes or coughs or sneezes,
these droplets of water, those particles,
what are they doing as they move through the air?
And actually this bit's pretty straightforward.
So in your computer simulation,
you're storing information about,
let's say, thousands of particles.
You can imagine it as a spreadsheet.
It probably isn't.
But you know, you've got this list of particles
with their properties, how heavy is the particle?
What's its velocity?
What are the forces that the particle is feeling?
And really it's just Newtonian mechanics.
So if you look at a particular particle,
what are the forces where it's got the it's weight
that's pulling the particle downwards,
it's got buoyancy that's pulling the particle upwards,
and it's got drag.
The next step is to ask, where will all these particles be
in a fraction of a second from now?
So you're just moving forwards in time,
like dit
but,
just a little bit forward in time,
where will they all be?
And that's an easy question to answer
because you've been tracking
the velocities of these particles,
and speed equals distance over time
or velocity equals displacement over time,
which we could rearrange to
displacement equals velocity times time.
So we can calculate the amount
that the particle is displaced.
In other words, how far does it move
as the velocity of the particle,
which is now multiplied by this a fraction of a second
that we're jumping forward in time.
Easy, sort of, except you might also
have to worry about acceleration
because maybe the velocity is changing
over that tiny fraction of a second.
In which case you need this equation,
which you might be familiar with.
It's got the acceleration term in there.
Maybe the acceleration is changing over time as well.
In which case you need a jerk term to add to the equation.
Here it is.
The thing to be aware of, though,
is that the acceleration term has a t squared in it.
And the jerk term has a t cubed in it.
And because we're moving just
a tiny fraction of a second into the future,
our t is very small.
And if t is very small, then t squared is very, very small,
and t cubed is very, very, very small.
So this term is very close to zero.
This term is quite close to zero.
You'll almost certainly be able to ignore this one
and probably this one, too.
So you've updated the positions of all the particles.
You also need to update the velocities
of all the particles because they're accelerating.
You know what the acceleration is
because you know the forces.
So you update all these particles.
Dit.
Slightly into the future.
And then you do it again.
Dit dit dit.
And these particles are incrementally moving
through your simulation.
So far that's quite straightforward,
except for that drag.
Because the drag force on the particle is related to
the motion of the air around the particle.
So this is the second thing that you have to do.
You have to also model the flow of air.
And this is where it gets bizarre.
One common method for modeling rhe flow of fluids
is called the Lattice Boltzmann method.
And the Lattice Boltzmann method
makes some remarkable assumptions about the world.
So it's called the Lattice Boltzmann method
because you assume this lattice of points in space,
and you say that atoms and molecules
are only allowed to exist at those lattice points.
You also say the atoms or molecules
are only allowed to have certain velocities.
In the case of a two-dimensional model,
which is a bit easier to understand visually,
there are only nine possible allowed velocities.
In 3D it's 27.
And actually there are some more exotic variations,
but let's just look at the two-dimensional case.
So you've got this lattice point here
and you've got a molecule on that lattice point,
and there are nine possible velocities that it can have.
And those velocities are
specifically the velocities that will take that molecule
from that lattice point to one of
the eight surrounding lattice points
or leave it stationary in the middle,
which is to say zero velocity,
within the specific time step
that you're using in your model
like we were using fractions of a second
when we were tracking particles earlier.
So it's really simple, like the next increment in time
you just move the molecule from the lattice point
to one of its neighboring ones or leave it where it is,
according to the velocity that it has
at that moment in your model.
And then the next thing you do,
before moving to the next time interval,
is to actually deal with collisions.
So the rule might be if two molecules
are coming into a lattice point like this,
then they'll leave like that.
Or if they're coming in like this, they'll leave like that.
Oh, and also you're not allowed to have
more than one atom or molecule with the same velocity
at the same lattice point.
What I'm actually describing here is a precursor
to the Lattice Boltzmann method
called the Lattice Gas Automata method.
What's remarkable is this Lattice Gas Automata model
has real predictive power.
Like it can behave in the same way
that you would expect a gas to
when it's following the Navier-Stokes equations,
which is wild, really.
There are some limitations to the Lattice Gas model,
and it's those limitations that the Lattice Boltzmann method
tries to address.
And it does that by saying,
instead of there either is or isn't an atom or molecule
at this lattice point with this specific velocity,
there either is or isn't an atom or molecule
at this lattice point with this particular velocity,
so that at any given moment in time
there'll be a collection of some molecules
with some of the possible velocities at each lattice point.
Instead you say that there's a probability
of a molecule at that lattice point,
having one of those nine possible velocities
and those probabilities change over time
as atoms and molecules migrate around the lattice
and collide with each other so on.
And so with the Lattice Boltzmann method,
these arrows can actually represent tens, hundreds,
thousands of atoms and molecules,
depending on the size of the lattice that you choose
and the density of the fluid you're trying to model.
Amazingly, this drastically oversimplified view of the world
produces realistic results.
Like here's a fluid flowing around a cylinder.
Here's a fluid flowing around two corners
modeled by the Lattice Boltzmann method.
To link all that back to calculating the drag
on a droplet of water that leaves someone's mouth,
well, this droplet of water in your model will be near
one of those lattice points
in the Lattice Boltzmann method of your model.
To figure out what the direction
of the airflow is at that point,
you just average out all those velocity vectors,
taking into account the probability of each one.
And once you know the velocity of airflow at that point,
you can calculate the drag.
This is called Lagrangian Particle Tracking,
by the way, if you wanna put a name to it.
Though, in certain circumstances,
you can do something much simpler.
You can assume that the particles are neutrally buoyant.
In other words, they never fall out.
They never lag behind.
They just perfectly follow the flow of air.
And that can be an appropriate thing to do
in certain circumstances.
For example, the first hospital simulations
were done in that way.
And in that case, you're looking at the worst case scenario
where the particles never fall out of the air.
How far can they travel?
If you're looking to investigate the effectiveness
of face masks and face shields,
it probably is best to go, what I call, full Lagrangian.
- We did some face shield simulation to find some
optimization to protect even more
the receiver of the droplets.
(laughing)
If I can say that.
And that's why,
when you are comparing, the first design
and the latest design of the face shield,
now you have some small spoilers on top.
Sometimes you have some spoilers on the bottom.
The mask has two good things.
The first one is, of course, the fact that you have
quite a lot of amount of particular
that will remain inside the mask.
So that's a good thing.
And the second thing, which is pretty good,
is the fact that you will slow down all the air,
and indeed the particles you will emit,
so at one point, even if you are coughing with the mask,
the aerosol you will emit will remain close to you.
And at one point, as you mentioned a bit earlier,
the convection of your body
will more propagate the particulars to the top.
- That's something I hadn't considered before.
By wearing a face mask you slow down
the particles leaving your body
so that they stay close to your body for longer
and they have a chance to be convected upwards,
away from the people around you
in a process powered by your own body heat.
A really big thank you to Dassault Systemes.
They don't just work with hospitals.
They work with schools, music venues,
all sorts of different clients.
Around the world we're in different states of lockdown.
Some things are open, some things are closed.
Imagine if venues like cinemas, theaters, restaurants
modeled the flow of air around their buildings
in a similar way,
and they could figure out ways to mitigate
the spread of coronavirus through the airborne route,
maybe these venues could open sooner than expected.
Dassault Systemes don't just model airflow.
They do all sorts of very interesting modeling stuff.
In fact, they told me about
one completely different thing that they do
that I thought was really interesting.
So I'm gonna make another video
about that in a few weeks' time.
So look out it for it.
And, oh, finally,
I'm sure you'll be pleased to know,
Emmanuel's grandmother made a full recovery.
- You know, it's creating a very nice momentum
and an exchange between the doctors and the engineers.
And it's something very valuable,
because it was something that was not done before.
Be curious, be smart, and share with the other.
That was very positive last year.
Yeah, and to me, changed my life.
- Yeah, yeah.
Great.
That's great.
I'm gonna,
that will be the final word of the video
will be from you saying that.