Variational Mode Decomposition-Based Nonstationary Coherent Structure
  Analysis for Spatiotemporal Data
Yuya Ohmichi
The modal analysis techniques face difficulties in handling nonstationary
phenomena. This paper presents a variational mode decomposition-based
nonstationary coherent structure (VMD-NCS) analysis that enables the extraction
and analysis of coherent structures in case of nonstationary phenomena from
high-dimensional spatiotemporal data. The VMD-NCS analysis decomposes the input
spatiotemporal data into intrinsic coherent structures (ICSs) that represent
nonstationary spatiotemporal patterns and exhibit coherence in both the spatial
and temporal directions. Furthermore, unlike many conventional modal analysis
techniques, the proposed method accounts for the temporal changes in the
spatial distribution with time. The performance of the VMD-NCS analysis was
validated based on the transient growth phenomena in the flow around a
cylinder. It was confirmed that the temporal changes in the spatial
distribution, depicting the transient growth of vortex shedding where
fluctuations arising in the far-wake region gradually approach the near-wake
region, were represented as a single ICS. Further, in the analysis of the
quasi-periodic flow field around a pitching airfoil, the temporal changes in
the spatial distribution and the amplitude of vortex shedding behind the
airfoil, influenced by the pitching motion of the airfoil, were captured as a
single ICS. Additionally, the impact of two parameters, adjusting the number of
ICSs ($K$) and the penalty factor related to the temporal coherence ($\alpha$),
was investigated. The results revealed that $K$ has a significant impact on the
VMD-NCS analysis results. In the case of a relatively high $K$, the VMD-NCS
analysis tends to extract more periodic spatiotemporal patterns resembling the
results of dynamic mode decomposition, whereas in the case of a small $K$, the
analysis tends to extract more nonstationary spatiotemporal patterns.