Dynamics-preserving compression for modal flow analysis

An efficient compression scheme for modal flow analysis is proposed and validated on data sequences of compressible flow through a linear turbomachinery blade row. The key feature of the compression scheme is a minimal, user-defined distortion of the mutual distance of any snapshot pair in phase space. Through this imposed feature, the model reduction process preserves the temporal dynamics contained in the data sequence, while still decreasing the spatial complexity. The mathematical foundation of the scheme is the fast Johnson-Lindenstrauss transformation (FJLT) which uses randomized projections and a tree-based spectral transform to accomplish the embedding of a high-dimensional data sequence into a lower-dimensional latent space. The compression scheme is coupled to a proper orthogonal decomposition and dynamic mode decomposition analysis of flow through a linear blade row. The application to a complex flow-field sequence demonstrates the efficacy of the scheme, where compression rates of two orders of magnitude are achieved, while incurring very small relative errors in the dominant temporal dynamics. This FJLT technique should be attractive to a wide range of modal analyses of large-scale and multi-physics fluid motion.