POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback

POD-based reduced-order hybrid simulation using the data-driven transfer function with... A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at $$Re \approx 2{,}000$$ R e ≈ 2 , 000 . Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback

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Springer Berlin Heidelberg
Copyright © 2014 by Springer-Verlag Berlin Heidelberg
Engineering; Engineering Fluid Dynamics; Fluid- and Aerodynamics; Engineering Thermodynamics, Heat and Mass Transfer
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