Hi my name is Danny Fritsch and I'm a graduate
student in the Crofton department of aerospace and ocean engineering.
Today, I'd like to talk briefly to you about the basics of my research, which attempts
to unearth the physics that drives aero and hydrodynamic noise.
Imagine for a moment that you are the captain of a warship in the Navy and you've been tasked
with finding enemy submarines that are trying to sneak past you unnoticed.
How would you do that?
Since the 2nd World War, we've been detecting subs by putting microphones in the water to
listen for them, which begs the question, what are we listening for?
In World War 2 we could listen for the chugging of the big diesel engines we used to drive
the submarines, but by the height of the Cold War, we'd begun using silent running nuclear
power to drive subs and so we would listen instead for the popping of cavitation bubbles
that form on the rapidly spinning propellers.
Today, we've figured out how to shield props
so they don't cavitate.
So what do we do then?
When any solid body moves through a fluid, the boundary where they interface moves slow
and behaves very strangely, which you can see in the bottom of this GIF.
We call this a boundary layer and it's a strong source of turbulence and turbulence noise.
Turbulence is just the chaotic swirling motion
of the fluid.
Now, while this is certainly of great interest to the Navy, it exists everywhere.
It's around airplanes as they come in and out of airports, it's on drones as they make
Amazon deliveries and record awesome Go Pro videos, it's on your car, what we normally
refer to as road noise and the reason you have to turn the radio up when you drive on
the highway.
It's also a big problem around wind turbine
farms.
Nobody wants to live near a wind farm which are surprisingly loud creating zoning and
p.r. problems for green energy companies.
In fact, the World Health Organization categorizes long exposure to wind farm sound as a health
hazard with several major symptoms, which also occur in people who live near busy airports
or highways.
This is a big problem and scientists and engineers
have been studying it for a long time but we still find it very hard to predict or prevent
this noise and that's because turbulence is by its very nature very hard to understand
and predict.
This plot simply simply shows volume as a function of pitch for results from an experiment
performed right here at Virginia Tech on a submarine-like body along with 6 peer reviewed
predictions by the biggest names in the field.
Now, 1st I'd like you to take note of the
large discrepancy among the predictions- 20 decibels and that's the difference between
a light pitter patter rainfall and traffic in a big city.
Also notice how the experimental data does not line up with any of the 6 predictions.
After researching this for decades how is our knowledge and predictive capability so
poor?
The answer is simply that the problem is just
too complicated.
This is a list of most of the variables that we think are causes and contributors to turbulence
noise and if that seems like an overwhelming list of random math symbols, you would be
correct.
And also bear in mind that along with all these variables we want to have predictive
capability for a huge variety of shapes, sizes, and conditions, so what we really need is
a way to take this overly complicated problem and turn it into a simple one, which is exactly
what me and my team are doing.
We work in the stability wind tunnel, which
is that big metal contraption behind Turner place.
We've designed a simple but elegant experiment for the stability tunnel that involves putting
a model of an airplane wing in the tunnel and rotating it around, which gives us very
fine control over the forces in the tunnel and then we study the turbulence on the walls
and how that generates noise.
And then we now have the power to recreate
virtually any condition imaginable by varying speed of the tunnel, the angle of our model,
the surface of the wall, and the point on that wall where we take measurements.
Thus, we can turn what is 20 or more variables into just 4.
And then we study those flows using the latest
technology, including laser diagnostics and extremely sensitive microphones, creating
a detailed and systematic picture of the turbulence and the noise it generates.
With this systematic approach we may finally be able to understand, predict, and even prevent
turbulence noise.
Thank you.