34 May/June 2022
Physics
8You can thank thermo-
capillary convection
for causing your food
to stick to your favorite
frying pan. That’s right,
physics can explain why,
sometimes, your meats
and veggies get stuck
during cooking. The
phenomenon causes hot
oil to bead, rupture, andspread to the outer edg-
es of a pan, leaving the
dreaded dry spot in the
middle. Research led by
Alexander Fedorchenko
from the Czech Academy
of Sciences discovered
that this type of con-
vection was a result of
uneven heating. Once the
cooking oil reaches a crit-ically thin point—which
in this study consistently
occurred in the middle
of the pan—it ruptures
due to loss of surface
tension. To alleviate the
problem, try using a
little more oil to make it
harder to reach that crit-
ically thin rupture point.
—Daisy HernandezMORE KITCHEN
PHYSICS: THIS
IS WHY YOUR
FOOD STICKS
TO THE PAN
back and forth between the two approaches, mov-
ing the cup with gusto in some situations, and more
delicately at other times. That left him wondering:
Where does the transition occur between in-phase
and antiphase synchronization?
To test his hypothesis, Wallace set up a simu-
lated mechanical experiment so that he could use
an unlimited number of test subjects. He chose to
set up a nonlinear model of a pendulum attached
to a moving cart. The cart stands in for the mug,
and the pendulum represents the sloshing coffee.
A nonlinear system takes into account all of the
chaotic behavior that can exist in our cup of coffee,
Wallace explains. Most real-world systems are non-
linear because they’re difficult to define and don’t
exist in a vacuum. While driving a car, for instance,
it will go 50 mph if you press down on the gas pedal,
but it won’t go 5,000 mph if you keep pressing
down. A linear system, by contrast, is much more
predictable: A spring system or a clock will always
move in the same regular fashion. Thinking math-
ematically, this checks out. The graph for the linear
equation y = x is always a straight line; meanwhile,
the graph for y = x^2 is a nonlinear equation that
looks like a curve, representing various solutions,
not just one.
Wallace and his team found that the transition
phase between each of the strategies was var-
ied, but that in both cases, humans could switch
between the approaches “abruptly and efficiently,”
according to their paper. The transition phase, as
expected, was the most chaotic, or unpredictable.
But humans veered away from that middle ground,
sticking closely to one approach or the other.
The researchers believe that they can implement
these controls in robots to make their movements
more predictable and reliable, adaptively handling
complex objects in ever-changing environments.
While it’s currently possible to program machines
to work on a binary basis—like humans vigorously
sloshing their cup of coffee or gently walking with
it—robots still aren’t refined enough to handle
switching between the two modes. On a manufactur-
ing line, for instance, hanging pendulum systems
are quite common, Wallace says. By controlling the
internal degrees of freedom in a manufacturing sys-
tem like this, a robotic arm can more reliably weld
the correct part without overshooting and fusing
another section.
This paradigm could also lead to better pros-
thetics, according to Ying-Cheng Lai, a professor at
ASU’s School of Electrical, Computer, and Energy
Engineering who was involved in the work. Let’s say
you have a prosthesis and you want to make a cup
of coffee. You have to get a signal from your brain
to the prosthetic, but it’s difficult to get the two to
match up. “If you have an idea of what you want the
prosthetic to do, like make the cup of coffee, you
could build in those sorts of natural intuitions that
the human has in a regular scenario to filter the
reference commands coming from the brain,” he
explains.
To make this all a reality, further work is still
required to better quantify the subtle changes
between approaches. Wallace says the team will
attempt to study systems with more degrees of
freedom, like a pendulum with another pendulum
hanging from it. If it all works out, we could one
day see robots that move with careful intention—
just like us.