  We propose a technique for performing spectral (in time) analysis of
spatially-resolved flowfield data, without needing any temporal resolution or
information. This is achieved by combining projection-based reduced-order
modeling with spectral proper orthogonal decomposition. In this method,
space-only proper orthogonal decomposition is first performed on velocity data
to identify a subspace onto which the known equations of motion are projected,
following standard Galerkin projection techniques. The resulting reduced-order
model is then utilized to generate time-resolved trajectories of data. Spectral
proper orthogonal decomposition (SPOD) is then applied to this model-generated
data to obtain a prediction of the spectral content of the system, while
predicted SPOD modes can be obtained by lifting back to the original velocity
field domain. This method is first demonstrated on a forced, randomly generated
linear system, before being applied to study and reconstruct the spectral
content of two-dimensional flow over two collinear flat plates perpendicular to
an oncoming flow. At the range of Reynolds numbers considered, this
configuration features an unsteady wake characterized by the formation and
interaction of vortical structures in the wake. Depending on the Reynolds
number, the wake can be periodic or feature broadband behavior, making it an
insightful test case to assess the performance of the proposed method. In
particular, we show that this method can accurately recover the spectral
content of periodic, quasi-periodic, and broadband flows without utilizing any
temporal information in the original data. To emphasize that temporal
resolution is not required, we show that the predictive accuracy of the
proposed method is robust to using temporally-subsampled data.
