Flow estimation of boundary layers using DNS-based wall shear information

Jones, Bernard J. T.; Kerrigan, Eric C.; Morrison, J. F.; Zaki, Tamer A.

doi:10.1080/00207179.2011.593000

Cited by 12 publications

(4 citation statements)

References 40 publications

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“…For the same reasons, it is also anticipated that the critical data resolution for reconstructing the turbulence is anisotropic -a matter that we will explore herein. Our focus is on reconstruction of turbulence at all scales using the nonlinear Navier-Stokes equations, and thus the critical data resolution is more restrictive than that for designing a reduced-order model for flow control (Jones et al 2011(Jones et al , 2015.…”

Section: Introductionmentioning

confidence: 99%

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State estimation in turbulent channel flow from limited observations

Wang

Zaki

2021

J. Fluid Mech.

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Section: Introductionmentioning

confidence: 99%

“…Our focus is on reconstruction of turbulence at all scales using the nonlinear Navier–Stokes equations, and thus the critical data resolution is more restrictive than that for designing a reduced-order model for flow control (Jones et al. 2011, 2015).…”

Section: Introductionmentioning

confidence: 99%

State estimation in turbulent channel flow from limited observations

Wang

Zaki

2021

J. Fluid Mech.

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“…Linear estimation has been applied to wall-bounded flows at laminar Reynolds numbers (Hoepffner et al 2005;Jones et al 2011) and at very low turbulent Reynolds numbers (Chevalier et al 2006). The contribution of this work is to apply similar tools to fully developed turbulence at a relatively high Reynolds number.…”

Section: Introductionmentioning

confidence: 99%

Estimating large-scale structures in wall turbulence using linear models

2018

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A dynamical systems approach is used to devise a linear estimation tool for channel flow at a friction Reynolds number of $Re_{\unicode[STIX]{x1D70F}}=1000$. The estimator uses time-resolved velocity measurements at a single wall-normal location to estimate the velocity field at other wall-normal locations (the data coming from direct numerical simulations). The estimation tool builds on the work of McKeon & Sharma (J. Fluid Mech., vol. 658, 2010, pp. 336–382) by using a Navier–Stokes-based linear model and treating any nonlinear terms as unknown forcings to an otherwise linear system. In this way nonlinearities are not ignored, but instead treated as an unknown model input. It is shown that, while the linear estimator qualitatively reproduces large-scale flow features, it tends to overpredict the amplitude of velocity fluctuations – particularly for structures that are long in the streamwise direction and thin in the spanwise direction. An alternative linear model is therefore formed in which a simple eddy viscosity is used to model the influence of the small-scale turbulent fluctuations on the large scales of interest. This modification improves the estimator performance significantly. Importantly, as well as improving the performance of the estimator, the linear model with eddy viscosity is also able to predict with reasonable accuracy the range of wavenumber pairs and the range of wall-normal heights over which the estimator will perform well.

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“…This problem is still reckoned as an outstanding issue , and progress in this direction is still required. Examples of works investigating this aspect are those of Podvin and Lumley , Hœpffner et al , and Jones et al , for wall bounded flows, the works of Lehmann et al , and Buffoni et al , for bluff body flows and the studies of Rowley and Juttijudata and Nagarajan et al , for cavity flows.…”

Section: Introductionmentioning

confidence: 99%

Wall‐based reduced‐order modelling

Lasagna

Tutty

2015

Numerical Methods in Fluids

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In this work we propose a novel approach to model order reduction for incompressible fluid flows that focuses on the spatio-temporal description of the stresses on the surface of a body, i.e. of the wall shear stress and of the wall pressure. The spatial representation of these two variables is given by a compact set of "wall basis functions", i.e. elementary basis functions defined on the wall. In this paper, these are derived using the well-known Proper Orthogonal Decomposition, to represent optimally the fluctuation energy of the pressure and shear stress. On the other hand, the functional structure of the dynamic model is derived from first principles using the vorticity form of the Navier-Stokes equations, yielding a set of nonlinear ordinary differential equations for the time-varying amplitudes of the wall shear stress basis functions. Coefficients of this model are then identified from simulation data. To complete the system, we show that the surface pressure distribution, i.e. the time-varying amplitudes of the wall pressure basis functions, can be derived from a quadratic model of the wall shear stress temporal coefficients, stemming from the Poisson equation for the pressure. This further step is crucial for the correct representation of the aerodynamic forces. As a paradigmatic example, we present our approach for the modelling of the free dynamics of the separated flow around a circular cylinder in the laminar regime, at Re = 200. Further implications and potentialities of the proposed approach are discussed.

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Flow estimation of boundary layers using DNS-based wall shear information

Cited by 12 publications

References 40 publications

State estimation in turbulent channel flow from limited observations

State estimation in turbulent channel flow from limited observations

Estimating large-scale structures in wall turbulence using linear models

Wall‐based reduced‐order modelling

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