  We propose an invariant feature space for the detection of viscous dominated
and turbulent regions (i.e., boundary layers and wakes). The developed
methodology uses the principal invariants of the strain and rotational rate
tensors as input to an unsupervised Machine Learning Gaussian mixture model.
The selected feature space is independent of the coordinate frame used to
generate the processed data, as it relies on the principal invariants of strain
and rotational rate, which are Galilean invariants. This methodology allows us
to identify two distinct flow regions: a viscous dominated, rotational region
(boundary layer and wake region) and an inviscid, irrotational region (outer
flow region). We test the methodology on a laminar and a turbulent (using Large
Eddy Simulation) case for flows past a circular cylinder at $Re=40$ and
$Re=3900$. The simulations have been conducted using a high-order nodal
Discontinuous Galerkin Spectral Element Method (DGSEM). The results obtained
are analysed to show that Gaussian mixture clustering provides an effective
identification method of viscous dominated and rotational regions in the flow.
We also include comparisons with traditional sensors to show that the proposed
clustering does not depend on the selection of an arbitrary threshold, as
required when using traditional sensors.
