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Stochastic receptivity analysis of boundary layer flow

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arxiv 1807.07759 v2 pith:6OMSFU75 submitted 2018-07-20 physics.flu-dyn math.APmath.DS

classification physics.flu-dynmath.APmath.DS
keywords stochasticanalysisflowboundaryeffectlayerlocallyparallel
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We utilize the externally forced linearized Navier-Stokes equations to study the receptivity of pre-transitional boundary layers to persistent sources of stochastic excitation. Stochastic forcing is used to model the effect of free-stream turbulence that enters at various wall-normal locations and the fluctuation dynamics are studied via linearized models that arise from locally parallel and global perspectives. In contrast to the widely used resolvent analysis that quantifies the amplification of deterministic disturbances at a given temporal frequency, our approach examines the steady-state response to stochastic excitation that is uncorrelated in time. In addition to stochastic forcing with identity covariance, we utilize the spatial spectrum of homogeneous isotropic turbulence to model the effect of free-stream turbulence. Even though locally parallel analysis does not account for the effect of the spatially evolving base flow, we demonstrate that it captures the essential mechanisms and the prevailing length-scales in stochastically forced boundary layer flows. On the other hand, global analysis, which accounts for the spatially evolving nature of the boundary layer flow, predicts the amplification of a cascade of streamwise scales throughout the streamwise domain. We show that the flow structures that can be extracted from a modal decomposition of the resulting velocity covariance matrix, can be closely captured by conducting locally parallel analysis at various streamwise locations and over different wall-parallel wavenumber pairs. Our approach does not rely on costly stochastic simulations and it provides insight into mechanisms for perturbation growth including the interaction of the slowly varying base flow with streaks and Tollmien-Schlichting waves.

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  1. Stochastic dynamical modeling of turbulent flows

    physics.flu-dyn 2019-08 conditional novelty 1.0 of 10

    A review showing that convex covariance completion with colored-in-time forcing lets stochastically forced linearized Navier-Stokes models reproduce second-order statistics of turbulent channel flow.

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