A Krylov-based proper orthogonal decomposition method for elastodynamics problems with isogeometric analysis

Liu, Xiaofei; Wang, Hu; Yu, Xiaolong; Wang, Chengjing

doi:10.1016/j.enganabound.2021.08.025

Cited by 4 publications

(1 citation statement)

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“…In the future, there is a need to investigate more novel DMD approaches such as the extended DMD [29] and the forward-backward DMD [30], with some extensions to account for control systems [31]. Additionally, extensions to the POD method can be investigated for extrapolation of the data, such as the Krylov-based POD [32]. These approaches show promise in terms of using a reduced-order-model-based acoustic prediction system, which may result in a faster turnaround time for design.…”

Section: Discussionmentioning

confidence: 99%

Data Reduction Technologies in Prediction of Propeller Noise

Afari,

Mankbadi

2024

Aerospace

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High-fidelity computations are often used in predicting the tonal and broadband noise of propellers and rotors associated with Advanced Air Mobility Vehicles (AAMVs). But LES is both CPU and storage intensive. We present here an investigation of the feasibility of reduction methods such as Proper Orthogonal Decomposition as well as Dynamic Mode Decomposition for reduction of data obtained via LES to be used further to obtain additional parameters. Specifically, we investigate how accurate reduced models of the high-fidelity computations can be used to predict the far-field noise. It is found that POD is capable of accurately reconstructing the parameters of interest with 15–40% of the total mode energies, whereas the DMD can only reconstruct primitive parameters such as velocity and pressure loosely. A rank truncation convergence criterion > 99.8% is needed for better performance of the DMD algorithm. In the far-field spectra, DMD can only predict the tonal contents in the lower and mid frequencies, while the POD can reproduce all frequencies of interest.

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