Dynamics-preserving compression for modal flow analysis

Numerical simulations of complex, multi-physics flow problems frequently produce large datasets, with the analysis of these datasets often constrained by the memory required to manipulate the data using standard decomposition algorithms. Many existing compression algorithms succeed at reducing spatial complexity but usually distort the underlying dynamics. We propose a dynamics-preserving compression technique, based on locality-sensitive hashing, that results in considerable dimensional reduction while controlling the distorting of the dynamics within a user-specified threshold. We apply this technique to a model turbomachinery flow and extract coherent modal structures covering proper orthogonal (POD) and dynamic modes (DMD). Compression rates of up to two orders of magnitudes can be achieved with, for example, only a one-percent distortion of the dynamics. This technique can be viewed as an alternative to Hankelized systems that encode the snapshot dynamics at the expense of increased spatial dimensionality. Extensions and further developments of the method will be discussed as well.