2016/03/08 by Jörn Sesterhenn, Sesterhenn, Jörn, Amir Shahirpour +1 · 1 citation
Engineering · Physics and Astronomy · #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #Nuclear Engineering Thermal-Hydraulics #Numerical Analysis (math.NA)
paper · pdf · doi:10.48550/arxiv.1603.02539
openalex publication_date 2016/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Temporal or spatial structures are readily extracted from complex data by modal decompositions like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). Subspaces of such decompositions serve as reduced order models and define either spatial structures in time or temporal structures in space. On the contrary, convecting phenomena pose a major problem to those decompositions. A structure traveling with a certain group velocity will be perceived as a plethora of modes in time or space respectively. This manifests itself for example in poorly decaying singular values when using a POD. The poor decay is counter-intuitive, since a single structure is expected to be represented by a few modes. The intuition proves to be correct and we show that in a properly chosen reference frame along the characteristics defined by the group velocity, a POD or DMD reduces moving structures to a few modes, as expected. Beyond serving as a reduced model, the resulting entity can be used to define a constant or minimally changing structure in turbulent flows. This can be interpreted as an empirical counterpart to exact coherent structures. We present the method and its application to a head vortex of a compressible starting jet.