Georgia Tech researchers used a thermal camera to record the variation in surface temperature (top) of a shape-memory alloy experiencing loading and unloading. By inputting this information into their thermo-mechanical model, the researchers were able to accurately predict internal temperature and stress distributions for the material, which is being investigated for use in constructing seismic-resistant structures. (Click image for high-resolution version. Credit: Reza Mirzaeifar) |
Recent
earthquake damage has exposed the vulnerability of existing structures
to strong ground movement. At the Georgia Institute of Technology,
researchers are analyzing shape-memory alloys for their potential use in
constructing seismic-resistant structures.
“Shape-memory
alloys exhibit unique characteristics that you would want for
earthquake-resistant building and bridge design and retrofit
applications: they have the ability to dissipate significant energy
without significant degradation or permanent deformation,” said Reginald
DesRoches, a professor in the School of Civil and Environmental
Engineering at Georgia Tech.
Georgia
Tech researchers have developed a model that combines thermodynamics
and mechanical equations to assess what happens when shape-memory alloys
are subjected to loading from strong motion. The researchers are using
the model to analyze how shape-memory alloys in a variety of
components—cables, bars, plates and helical springs—respond to different
loading conditions. From that information, they can determine the
optimal characteristics of the material for earthquake applications.
The
model was developed by DesRoches, School of Mechanical Engineering
graduate student Reza Mirzaeifar, School of Civil and Environmental
Engineering associate professor Arash Yavari, and School of Mechanical
Engineering and School of Materials Science and Engineering professor Ken Gall.
A paper describing the thermo-mechanical model was published online Feb. 3 in the International Journal of Non-Linear Mechanics. This research was supported by the Transportation Research Board IDEA program.
To
improve the performance of structures during earthquakes, researchers
around the world have been investigating the use of “smart” materials,
such as shape-memory alloys, which can bounce back after experiencing
large loads. The most common shape-memory alloys are made of metal
mixtures containing copper-zinc-aluminum-nickel, copper-aluminum-nickel
or nickel-titanium. Potential applications of shape-memory alloys in
bridge and building structures include their use in bearings, columns
and beams, or connecting elements between beams and columns. But before
this class of materials can be used, the effect of extreme and
repetitive loads on these materials must be thoroughly examined.
“For
standard civil engineering materials, you can use mechanics to look at
force and displacement to measure stress and strain, but for this class
of shape-memory alloys that changes properties when it undergoes loading
and unloading, you have to consider thermodynamics and mechanics,”
explained Yavari.
The
Georgia Tech team found that the generation and absorption of heat
during loading and unloading caused a temperature gradient in
shape-memory alloys, which caused a non-uniform stress distribution in
the material even when the strain was uniform.
“Shape-memory
alloys previously examined in detail were really thin wires, which can
exchange heat with the ambient environment rapidly and no temperature
change is seen,” said Mirzaeifar. “When you start to examine alloys in
components large enough to be used in civil engineering applications,
the internal temperature is no longer uniform and needs to be taken into
account.”
To
predict the internal temperature distribution of shape-memory alloys
under loading-unloading cycles, which could then be used to determine
the stress distribution, the researchers developed a model that used the
surface thermal boundary conditions, diameter and loading rate of the
alloy as inputs.
The
team included ambient conditions in the model because shape-memory
alloys for seismic applications could operate in a variety of
environments—such as water if used in bridge structures or air if used
in building structures—which would produce different rates of heat
transfer. The researchers used a thermal camera to record the variation
in surface temperature of shape-memory alloys experiencing loading and
unloading.
Using
their model, the researchers were able to accurately predict internal
temperature and stress distributions for shape-memory alloys. The model
results were verified with experimental tests. In one test, they found
that a shape-memory alloy loaded at a very slow rate had time to
exchange the heat created with the ambient environment and exhibited
uniform stress. If it was loaded very rapidly, it did not have enough
time to exchange the heat, leading to a non-uniform stress distribution.
“Our
analytical solutions are exact, fast and capable of simulating the
complicated coupled thermo-mechanical response of shape-memory alloys
considering temperature changes and loading rate dependency,” said
Mirzaeifar.
In
future work, the researchers plan to examine more complicated shapes
and the effects of combination loading—tension, bending and torsion—to
optimize shape-memory alloys for earthquake applications.
Coupled thermo-mechanical analysis of shape memory alloy circular bars in pure torsion
