The polarisation method and the method of equivalent nodal forces: A comparison with other modal decomposition methods in the analysis of coupled instabilities

2021 ◽  
Vol 158 ◽  
pp. 107174
Author(s):  
Xilin Li
Author(s):  
Jiawei Hu ◽  
Yangang Wang ◽  
Hanru Liu ◽  
Weixiong Chen ◽  
Yong Xu

The present work investigated the vortex structure and fluctuation frequency characteristics generated by boundary layer separation of a high-load compressor cascade using modal decomposition methods. The dominant modes and dynamic behaviors of unsteady flow in the cascade were obtained, and the differences of three modal decomposition methods (Proper Orthogonal Decomposition, Dynamic Mode Decomposition and Spectral Proper Orthogonal Decomposition) in feature recognition of cascade flow were discussed. The results show that:(1) The POD method can accurately extract the dominant spatial structure of the flow field, but the modal coefficients are multi-frequency coupled, which makes the dominant modal characteristics of cascade flow unclear. (2) The standard DMD method can obtain the spatial-temporal single frequency mode of cascade flow, as well as their growth rates and frequencies. However, this method is likely to capture the suboptimal mode of large amplitude with large attenuation rate, and fails to obtain the high-frequency coherent structure, which makes it impossible to obtain the dominant feature with limited mode number. (3) The SPOD method, based on spectral characteristics, can obtain spatial and temporal single frequency modes, and there is no modal screening problem. The use of spectral estimation method (SPOD) reduces the sensitivity to numerical noise. This method can obtain the low-rank behavior of cascade flow, which is helpful to understand cascade flow mechanisms. Therefore, SPOD method is more advantageous for the modal analysis of unsteady separated flow in high-load compressor cascade.


2019 ◽  
Vol 143 ◽  
pp. 106229 ◽  
Author(s):  
Jurgen Becque ◽  
Xilin Li ◽  
Buick Davison

2022 ◽  
Author(s):  
Molly H. Donovan ◽  
Markus P. Rumpfkeil ◽  
Sidaard Gunasekaran ◽  
Christopher R. Marks

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