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.

中文翻译：

---

用于水声分析的 LES 数据驱动降阶方法的比较

在这项工作中，动态模式分解 (DMD) 和适当正交分解 (POD) 方法应用于使用大涡模拟 (LES) 以及 Ffowcs Williams 和 Hawkings (FWH) 类比计算的水声数据集。首先，通过模态分解分析对流场进行低维描述。讨论了对 DMD 和 POD 基截断等级的敏感性，并提供了大量数据集以证明两种算法能够以支持准确噪声评估所需的所有空间和时间频率重建流场。结果表明，虽然 DMD 能够在相同数量的采用模式下捕获尾流区域中更精细的相干结构，与 DMD 对应物相比，使用 POD 重建的流场表现出更小的全局时空误差。其次，使用一半快照生成的一组单独的 DMD 和 POD 模式分别用于两个数据驱动的简化模型，基于 DMD 中间转换和带插值的 POD (PODI)。在这方面，结果证实，两种简化方法对流场的预测特性都足够准确，PODI 结果优于 DMD 方法。这推断，对于当前设置，与 DMD 中的积分和线性回归操作引起的误差相比，由 PODI 中的插值误差引起的差异相对较低。最后，