  Dynamic mode decomposition (DMD) is a data-driven technique widely used to
analyze and model fluid problems including transonic buffet flows. Despite its
strengths, DMD is known to suffer from sensitivities to the selected settings
and the characteristics of the employed data. In this work, we closely examine
the aforementioned sensitivities, identify possible pitfalls, and provide best
practices to robustly perform DMD on a flow exhibiting transonic shock buffet.
Specifically, we assess various DMD variants and test their sensitivity to the
POD rank truncation and the sampling rate. A critical enabler to our analysis
is a new presentation of the DMD algorithm as a modular framework consisting of
five distinct steps. The tests also highlight the existing dangers of aliasing,
when the sampling rate is too low. Finally, a list of practical recommendations
and guidelines on how to accurately and robustly perform DMD on a transonic
buffet flow is provided.
