On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis

TL;DR

This paper compares DMD and POD data-driven reduced order models applied to LES-based hydroacoustic data, evaluating their accuracy in flow reconstruction and noise prediction, and finds PODI generally outperforms DMD in predictive accuracy.

Contribution

It provides a comprehensive comparison of DMD and POD-based reduced models for hydroacoustic analysis, including their reconstruction and predictive capabilities.

Findings

POD exhibits smaller global spatiotemporal errors than DMD.

PODI provides more accurate flow predictions than DMD.

Both models effectively predict acoustic noise from flow data.

Abstract

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.