  Scale-resolving flow simulations often feature several million [thousand]
spatial [temporal] discrete degrees of freedom. When storing or re-using these
data, e.g., to subsequently train some sort of data-based surrogate or compute
consistent adjoint flow solutions, a brute-force storage approach is
practically impossible. Therefore, -- mandatory incremental -- Reduced Order
Modeling (ROM) approaches are an attractive alternative since only a specific
time horizon is effectively stored. This bunched flow solution is then used to
enhance the already computed ROM so that the allocated memory can be released
and the procedure repeats.
  This paper utilizes an incremental truncated Singular Value Decomposition
(itSVD) procedure to compress flow data resulting from scale-resolving flow
simulations. To this end, two scenarios are considered, referring to an
academic Large Eddy Simulation (LES) around a circular cylinder at Re=1.4E+05
as well as an industrial case that employs a hybrid filtered/averaged Detached
Eddy Simulation (DES) on the flow around the superstructure of a full-scale
feeder ship at Re=5E+08.
  The paper's central focus is on an aspect of practical relevance: how much
information of the computed scale-resolving solution should be used by the ROM,
i.e., how much redundancy occurs in the resolved turbulent fluctuations that
favors ROM. In the course of the tSVD employed, this goes hand in hand with the
question of "how many singular values of the snapshot-matrix should be
neglected (or considered)" -- without (a) re-running the simulation several
times in a try-and-error procedure and (b) still obtain compressed results
below the model and discretization error. An adaptive strategy is utilized to
obtain a fully adaptive data reduction of O(95) percent via a computational
overhead of O(10) percent with a mean accuracy of reconstructed local and
global flow data of O(0.1) percent.
