  In this work, Dynamic Mode Decomposition (DMD) and Proper Orthogonal
Decomposition (POD) methodologies are applied to hydroacoustic dataset computed
using Large Eddy Simulation (LES) coupled with Ffowcs Williams and Hawkings
(FWH) analogy. First, a low-dimensional description of the flow fields is
presented with modal decomposition analysis. Sensitivity towards the DMD and
POD bases truncation rank is discussed, and extensive dataset is provided to
demonstrate the ability of both algorithms to reconstruct the flow fields with
all the spatial and temporal frequencies necessary to support accurate noise
evaluation. Results show that while DMD is capable to capture finer coherent
structures in the wake region for the same amount of employed modes,
reconstructed flow fields using POD exhibit smaller magnitudes of global
spatiotemporal errors compared with DMD counterparts. Second, a separate set of
DMD and POD modes generated using half the snapshots is employed into two
data-driven reduced models respectively, based on DMD mid cast and POD with
Interpolation (PODI). In that regard, results confirm that the predictive
character of both reduced approaches on the flow fields is sufficiently
accurate, with a relative superiority of PODI results over DMD ones. This
infers that, discrepancies induced due to interpolation errors in PODI is
relatively low compared with errors induced by integration and linear
regression operations in DMD, for the present setup. Finally, a post processing
analysis on the evaluation of FWH acoustic signals utilizing reduced fluid
dynamic fields as input demonstrates that both DMD and PODI data-driven reduced
models are efficient and sufficiently accurate in predicting acoustic noises.
