PRODUCTS & SERVICES
AEROSPACETESTINGINTERNATIONAL.COM // MARCH 2020 95
modal testing
made easy
Modal testing is a very useful technique for
verifying and understanding the dynamic
behavior of complex structures
Its principle is to characterize a structure by
its resonant modes with associated
resonant frequencies, damping and mode
shapes, which help identify potential issues
or weaknesses in the design.
This technique has been widely used for
decades on various types of structures; from
small components to fully integrated aircraft
as part of GVT (Ground Vibration Testing)
campaigns, supporting the aircraft
certification process. Next to this, the modal
survey technique, commonly used in the
space industry, helps verify the structural
dynamics performance of spacecraft and
space launchers.
However, efficiently conducting a modal
test can be a difficult task. When processing
measurement data to extract modal
parameters, several choices must be made by
the engineer, making it difficult to get reliable
and consistent data. For instance, in the case
of highly damped structures or large
structures instrumented with hundreds of
sensors with high modal density, properly
choosing the poles of the modal model is
challenging — it is not always straightforward
and might require deep expertise. The
introduction of the Polymax
algorithm by Siemens has
already led to significant
improvement, providing
more clarity in the
stabilization diagrams. But verifying that the
modal model fits well with the measured data
is still a tedious task that might require many
successive iterations.
With the introduction of the MLMM
(Maximum Likelihood Modal Model) algorithm
into the Simcenter Testing Solutions portfolio,
Siemens is making modal testing easier and
more accessible to non-expert users, built on
top of the current modal testing process.
Starting from a set of measured vibration
data, the MLMM algorithm automatically
iterates on the parameters of the modal
model to improve its accuracy and the fit to
the experimental data for all measurement
positions and for the frequency range of
interest. This allows the user to get rid of the
lengthy and tedious manual iterations.
Not only that, MLMM brings extra accuracy
in terms of resonant frequency and damping
estimates and accelerates processing tasks
when dealing with a higher level of
complexity of measured structures, allowing
non-expert users to deliver more consistent
data. The improvement of the modal model
accuracy with the MLMM algorithm is
especially remarkable when constraints,
such as reciprocity or non-complexity, are
imposed on the modes in view of finite
element model correlation.
A typical scenario is when many sensors
and exciters are used at the same time during
a vibration test, which can cause issues in
the modal estimation process. By using the
MLMM optimization, this is automatically
solved, and the local dynamic behavior is
better represented. In the example shown in
figures 1 and 2, where modes are extracted
during a GVT on a complete aircraft, the
correlation between measured FRFs
(frequency response functions) and FRFs
synthesized with the modal parameters
significantly increases, especially in the case
of real and reciprocal modes.
Thanks to MLMM, a more consistent model
is obtained, which can then be used with
higher confidence during the aircraft
verification process, while keeping the
existing testing processes unchanged.
Regardless of whether the test involves
simple or complex modal scenarios, MLMM is
a useful technique to improve the way modal
testing is performed and help test engineers
achieve better results faster. With the new
MLMM technique, modal testing has never
been so easy. \\
1 // Large scale modal test
2 // Improvement of
frequency response
functions curve fitting
with the MLMM
modal technique
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