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The correlation and extraction of coherent structures from a turbulent flowis a principle objective of data-driven modal decomposition techniques. TheConditional space-time Proper Orthogonal Decomposition (CPOD) offers insightinto transient dynamics, revealing the causation of specific flow phenomenon -or events, in a customizable manner. This work exploits the temporal evolutionof CPOD modes in a reduced subspace, resulting in new extensions andadaptations that meet or exceed the capabilities of other decompositionmethods. Chiefly, it is demonstrated that the subsequent application of dynamicmode decomposition (DMD) to CPOD modes, provides a flexible tool to investigatetargeted flow instabilities, both tonal and convective in nature. By extendingthe CPOD time-horizon to educe the former type, it is shown that CPOD-DMD canexactly reproduce Spectral POD modes. Regarding the latter, a multi-resolutionframework (CPOD-mrDMD) yields a refined "cause and effect" stability analysis,capable of diagnosing the natural forcing mechanisms within the flow, and theresulting unstable modes. In a separate application, CPOD properties areappreciated in the context of reduced order models, with an example ofreal-time flow prediction of extreme events derived from an active sensorcorrelated to a CPOD mode. The various CPOD functions and perspectives in thiswork are demonstrated on: the nonlinear chaotic Lorenz system, 3D intermittentturbulent spots in supersonic boundary layer transition, Schlieren videoprocessing of unstarted inlet buzz, the aeroacoustic feedback forcing of aresonating impinging jet, and prediction of intermittent bluff-body wakestructures impinging on a channel wall.

Conditional space-time POD extensions for stability and prediction analysis