Despite the growing computer power and increasing sophistication of
computational models, any design meant operate in the real world requires some
form of experimental validation. The idealist modeller, me included, wants to
believe that computer simulation will replace all forms of experimental testing
and thereby allow for much faster design cycles. The issue with this is that
random imperfections, and most importantly their concurrence, are very hard to
account for robustly, especially when operating in nonlinear domains. As a
result, the quantity and quality of both computational and experimental
validation have increased in lockstep over the few last decades.
In “The Wind and Beyond”, the autobiography of Theodore von Kármán,
one of the pre-eminent aerospace engineers and scientists of the 20th century,
von Kármán recounts a telling episode regarding the role of wind tunnel testing
in the development of the Douglas DC-3, the first American commercial jetliner.
Early versions of the DC-3 faced a problem with aerodynamic instabilities that
could throw the airplane out of control. A similar problem had been noticed
earlier on the Northrop Alpha airplane, which, like the DC-3, featured a wing
that was attached to the underside of the fuselage. When two of von Kármán’s
assistants, Major Klein and Clark Millikan, subjected a model of the Alpha to
high winds in a wind tunnel, the model aircraft started to sway and shake violently.
In the following investigation, Klein and Millikan found that the sharp corner
at the connection between the wing and fuselage decelerated the air as it
flowed past, causing boundary layer separation and a wake of eddies. As these
eddies broke away from the trailing edge of the wing, they adversely impacted
the flow over the horizontal stabiliser and vertical tail fin at the rear of
the aircraft and resulted in uncontrollable vibrations.
The Northrop Alpha plane with the Kármán fillet at the wing-fuselage
joint
Fortunately, Theodore von Kármán was world-renowned, among other things,
for his work on eddies and especially the so-called von Kármán Vortex Street.
Von Kármán therefore intuitively realised what had to be done to eliminate the
creation of these eddies. Von Kármán and his colleagues fitted a small fairing,
a filling if you like, to the connection between the wing and the fuselage to
smooth out the eddies. This became one of the
textbook examples of how wind tunnel findings could be applied in a practical way
to iron out problems with an aircraft. When French engineers learned of the
device from von Kármán at a conference a few years later, they were so
enamoured that such a simple idea could solve such a big problem that they
named the fillet a “Kármán”.
When testing the aerodynamics of aircraft, the wind tunnel is
indispensable. The Wright brothers built their own wind tunnel to validate the
research data on airfoils that had been
recorded throughout the 19th century. One of the most important pieces of equipment
in the early days of NACA (now NASA) was a variable-density wind tunnel, which
by pressurising the air, allowed realistic operating conditions to be simulated
on 1/20th geometrically-scaled models.
NACA variable density wind tunnel
This brings us to an important point: How do you test the
aerodynamics of an aircraft in a wind-tunnel?
Do you need to build individual wind-tunnels big enough to fit a
particular aircraft? Or can you use a smaller multi-purpose wind tunnel to test
small-scale models of the actual aircraft? If this is the case, how
representative is the collected data of the actual flying aircraft?
Luckily we can make use of some clever mathematics, known as dimensional
analysis, to make our life a little easier. The key idea behind dimensional
analysis is to define a set of dimensionless parameters that govern the
physical behaviour of the phenomenon being studied, purely by identifying the
fundamental dimensions (time, length and mass in aerodynamics) that are at
play. This is best illustrated by an example.
The United States developed the atomic bomb during WWII under the
greatest security precautions. Even many years after the first test of 1945 in
the desert of New Mexico, the total amount of energy released during the
explosion remained unknown. The British scientist G.I. Taylor then famously
estimated the total amount of energy released by the explosion simply by using
available pictures showing the explosion plume at different time stamps after
detonation.
Nuclear explosion time frames
By assuming that the shock wave could be modelled as a perfect sphere,
Taylor posited that the size of the plume, i.e. the radius , should depend on the energy of the explosion, the time after detonation and the density of the surrounding air.
In dimensional analysis we proceed to define the fundamental units or
dimensions that quantify our variables. So in this case:
● Radius is defined by a distance, and therefore the units are length, i.e.
● The units of time are, you guessed it, time, i.e.
● Energy is force times distance, where a force is mass times acceleration, and acceleration is distance divided by time squared i.e.
● Density is mass divided by volume, where volume is a distance cubed, i.e.
Having determined all our variables in the fundamental dimensions of
distance, time and mass, we now attempt to relate the radius of the explosion
to the energy, density and time. If we assume that the radius is proportional
to these three variables, then dividing the radius by the product of the other
three variables must result in a dimensionless number. Hence,
Or alternatively, all fundamental dimensions in the above fraction must
cancel:
For all units to disappear we need:
and solving this system gives:
Therefore the shock wave radius is given by
and by re-arranging
where .
So, we have an expression that relates the energy of the explosion to
the radius, the density of air and time after detonation, which were all
available to Taylor from the individual time stamps (these provided a diameter
estimate and the time after detonation. The density of the air was known).
In the example above, specific calculations of also require an estimate of the constant . In aerodynamics, we are typically interested in quantifying
the constant itself using the variables at hand. Hence, by analogy with the
above example, we would know the energy, the density, radius and time and then
calculate a value for the constant under these conditions. As the constant is
dimensionless, it allows us to make an unbiased judgement of the flow
conditions for entirely different and unrelated problems.
The most famous dimensionless number in aerodynamics is probably the
Reynolds number which quantifies the nature of the flow, i.e. is it laminar
(nice and orderly in layers that do not mix), or is it turbulent, or somewhere
in between?
In determining aerodynamic forces, two of the important variables we
want to understand and quantify are the lift and drag. Particularly, we want to
determine how the lift and drag vary with independent parameters such as the
flight velocity, wing area and the properties of the surrounding area.
Using a similar method as above, it can be shown that the two primary
dimensionless variables are the lift () and
drag coefficients (),
which are defined in terms of lift (), drag
(), flight velocity (),
static fluid density () and
wing area ().
Lift coefficient:
Drag coefficient:
where is known as the
dynamic pressure of a fluid in motion. When the dynamic pressure is multiplied
by the wing area, , we
are left with units of force which cancel the unit of lift () and drag (), thus
making and dimensionless.
As long as the geometry of our vehicle remains the same (scaling up and
down at constant ratio of relative dimensions, e.g. length, width, height, wing
span, chord etc.), these two parameters are only dependent on two other
dimensionless variables: the Reynolds number
where and are characteristic flow velocity and length (usually
aerofoil chord or wingspan), and the the Mach
Number
which is the ratio of aircraft speed to the
local speed of sound.
Let’s recap what we have developed until now. We have two dimensionless
parameters, the lift and drag coefficients, which measure the amount of lift
and drag an airfoil or flight vehicle
creates normalised by the conditions of the surrounding fluid () and the geometry of
the lifting surface ().
Hence, these dimensionless parameters allow us to make a fair comparison of the
performance of different airfoils regardless
of their size. Comparing the and of
two different airfoils requires that the
operating conditions be comparable. They do not have to be exactly the same in
terms of air speed, density and temperature but their dimensionless quantities,
namely the Mach number and Reynolds number, need to be equal.
As an example consider a prototype aircraft flying at altitude and a
scaled version of the same aircraft in a wind tunnel. The model and prototype
aircraft have the same geometrical shape and only vary in terms of their
absolute dimensions and the operating conditions. If the values of Reynolds
number and Mach number of the flow are the same for both, then the flows are
called dynamically similar, and as the geometry of the two aircraft are scaled
version of each other, it follows that the lift and drag coefficients must be
the same too. This concept of dynamic similarity is crucial for wind-tunnel
experiments as it allows engineers to create small-scale models of full-sized
aircraft and reliably predict their aerodynamic qualities in a wind tunnel.
This of course means that the wind tunnel needs to be operated at
entirely different temperatures and pressures than the operating conditions at
altitude. As long as the dimensions of the model remain in proportion upon
scaling up or down, the model wing area scales with the square of the wing
chord, i.e. is
proportional to . We know from the explanation above that
for a certain combination of Mach number and Reynolds number the lift and drag
coefficients are fixed.
Using the definition of and the lift is given by
and the drag by
The lift and drag created by an aircraft or model under constant Mach
number and Reynolds number scale with the wing area or the wing chord squared.
Rearranging the equation for the Reynolds number, the wing chord can in fact be
shown to be proportional to the operating temperature and pressure of the fluid
flow. So by rearranging the Reynolds number equation:
and from the fundamental gas equation
and the Mach Number we have
such that we can reformulate the chord
length as follows
Hence, the chord of the model is inversely proportional to the fluid
pressure and directly proportional to the square of the fluid temperature.
Thus, maximising the pressure and reducing the temperature (maximum fluid
density) reduces the required size of the model and the overall aerodynamic
forces. The was the concept behind NACA’s
early variable density tunnel and is still exploited in modern cryogenic wind
tunnels.