Kinematic Simulation of Fully Developed Turbulent Channel Flow

Clark, Neil R.; Vassilicos, J. C.

doi:10.1007/s10494-010-9316-x

“…These typically involve the superposition of randomized Fourier modes that obey prescribed one-and two-point correlations (Fung et al 1992;Elliott & Majda 1996;Malik & Vassilicos 1999;Fung & Vassilicos 1998;Cambon et al 2004;Clark & Vassilicos 2011). Likewise, generating statistically consistent turbulent inflow condi- tions for numerical simulations of transitional/turbulent flows as well as flow control has been a topic of great interest (Keating et al 2004;Hoepffner et al 2011).…”

Section: Kinematic Simulation Of Turbulent Flow and The Turbulent Infmentioning

confidence: 99%

Colour of turbulence

Zare

¹

,

Jovanović

²

,

Georgiou

³

2017

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In this paper, we address the problem of how to account for second-order statistics of turbulent flows using low-complexity stochastic dynamical models based on the linearized Navier-Stokes equations. The complexity is quantified by the number of degrees of freedom in the linearized evolution model that are directly influenced by stochastic excitation sources. For the case where only a subset of velocity correlations are known, we develop a framework to complete unavailable second-order statistics in a way that is consistent with linearization around turbulent mean velocity. In general, white-in-time stochastic forcing is not sufficient to explain turbulent flow statistics. We develop models for colored-intime forcing using a maximum entropy formulation together with a regularization that serves as a proxy for rank minimization. We show that colored-in-time excitation of the Navier-Stokes equations can also be interpreted as a low-rank modification to the generator of the linearized dynamics. Our method provides a data-driven refinement of models that originate from first principles and captures complex dynamics of turbulent flows in a way that is tractable for analysis, optimization, and control design.

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“…Kinematic simulations of fully developed turbulence have been extensively used to generate synthetic flow fields. These typically involve the superposition of randomized Fourier modes that obey prescribed one-and two-point correlations (Fung et al 1992;Elliott & Majda 1996;Malik & Vassilicos 1999;Fung & Vassilicos 1998;Cambon et al 2004;Clark & Vassilicos 2011). Likewise, generating statistically consistent turbulent inflow condi- For the linearized dynamics of fluctuations around turbulent mean velocity, the appropriate forcing is sought to reproduce partially available velocity correlations and complete the statistical signature of the turbulent flow field.…”

Section: Kinematic Simulation Of Turbulent Flow and The Turbulent Inf...mentioning

confidence: 99%

Color of turbulence

Zare,

Jovanović,

Georgiou

2016

Preprint

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In this paper, we address the problem of how to account for second-order statistics of turbulent flows using low-complexity stochastic dynamical models based on the linearized Navier-Stokes equations. The complexity is quantified by the number of degrees of freedom in the linearized evolution model that are directly influenced by stochastic excitation sources. For the case where only a subset of velocity correlations are known, we develop a framework to complete unavailable second-order statistics in a way that is consistent with linearization around turbulent mean velocity. In general, white-in-time stochastic forcing is not sufficient to explain turbulent flow statistics. We develop models for colored-intime forcing using a maximum entropy formulation together with a regularization that serves as a proxy for rank minimization. We show that colored-in-time excitation of the Navier-Stokes equations can also be interpreted as a low-rank modification to the generator of the linearized dynamics. Our method provides a data-driven refinement of models that originate from first principles and captures complex dynamics of turbulent flows in a way that is tractable for analysis, optimization, and control design.

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Particle transport in a turbulent boundary layer: Non-local closures for particle dispersion tensors accounting for particle-wall interactions

Bragg

¹

,

Swailes

²

,

Skartlien

³

2012

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Continuum equations derived from a probability density function kinetic equation contain dispersion tensors that describe the interaction between inertial particles and the underlying turbulent flow in which they are transported. These tensors require closure treatment and recent work has shown that traditional closure approximations perform poorly when applied to the case of particle dispersion in turbulent boundary layers. The dispersion tensors are intrinsically non-local, being sensitive to both the strong inhomogeneity of wall-bounded turbulence and the influence of particle-wall collisions. A new strategy for constructing non-local closure models is presented to account for such influences. An important feature of the approach is that it utilizes exactly the same input parameters required for the traditional closures. Differences between the two approaches are therefore a reflection of the improved closure strategy, rather than a consequence of improved or additional input data. Predictions from both new and standard models are compared with benchmark statistics obtained from the simulation of particle trajectories in a synthetic boundary layer flow, generated via an inhomogeneous kinematic simulation technique. The results demonstrate the significant advance offered by the new approach.

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Kinematic Simulation of Fully Developed Turbulent Channel Flow

Cited by 4 publications

References 29 publications

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Particle transport in a turbulent boundary layer: Non-local closures for particle dispersion tensors accounting for particle-wall interactions

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