On the Use of LES with a Dynamic Subgrid-Scale Model for Optimal Control of Wall Bounded Turbulence

Collis, S. Scott; Chang, Yong

doi:10.1007/978-94-011-4513-8_9

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…Through the dynamic procedure, the Smagorinsky coefficient automatically adjusts in response to the state of the controlled flow. Our initial investigations were the first to demonstrate the viability of the dynamic LES procedure for accurate and efficient prediction of controlled turbulent flows (Collis and Chang, 1999) through careful comparisons to DNS (Chang, 2000;.…”

Section: Introductionmentioning

confidence: 99%

Viscous effects in control of near-wall turbulence

2002

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Prior studies of wall bounded turbulence control have utilized Direct Numerical Simulation (DNS) which has limited investigations to low Reynolds numbers where viscous effects may play an important role. The current paper utilizes Large Eddy Simulation (LES) with the dynamic subgrid-scale model to explore the influence of viscosity on one popular turbulence control strategy, opposition control, that has been extensively studied using low Reynolds number DNS. Exploiting the efficiency of LES, opposition control is applied to fully developed turbulent flow in a planar channel for turbulent Reynolds numbers in the range Re τ = 100 to 720. As Reynolds number increases, the predicted drag reduction drops from 30% at Re τ = 100 to 19% at Re τ = 720. Furthermore, the ratio of power saved to power input drops by more than a factor of four when Reynolds number increases over this range, indicating that the drag reduction mechanism in opposition control is indeed less effective at higher Reynolds numbers. However, for sufficiently high Reynolds numbers, Re τ > 400, the ratio of power saved to power input becomes constant at a value near 40 indicating that opposition control is a viable turbulence control strategy at high Reynolds numbers.

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Section: Introductionmentioning

confidence: 99%

Viscous effects in control of near-wall turbulence

2002

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“…Moreover, it is likely that the large scales for the boundary layer are different from that in the wake region [27,45]. First, using the local refinement capabilities, we select a mesh and polynomial order to sufficiently represent the features of the boundary layer and wakes.…”

Section: Vms Advantages and Potentialmentioning

confidence: 99%

A mathematical framework for multiscale science and engineering : the variational multiscale method and interscale transfer operators.

Shadid¹,

Lehoucq²,

Christon³

et al. 2004

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This report is a collection of documents written as part of the Laboratory Directed Research and Development (LDRD) project A Mathematical Framework for Multiscale Science and Engineering: The Variational Multiscale Method and Interscale Transfer Operators. We present developments in two categories of multiscale mathematics and analysis. The first, continuum-to-continuum (CtC) multiscale, includes problems that allow application of the same continuum model at all scales with the primary barrier to simulation being computing resources. The second, atomistic-to-continuum (AtC) multiscale, represents applications where detailed physics at the atomistic or molecular level must be simulated to resolve the small scales, but the effect on and coupling to the continuum level is frequently unclear.

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“…The papers [1][2][3][4][5][6][7][8][9][10][11] present a sample of recent activities in the former category and References [12][13][14][15][16][17] illustrate activities in the latter category. To the authors' knowledge, the present paper documents the ÿrst approach to the optimal boundary control of unsteady compressible viscous ows.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in References [1,7,10,11] the regularization term for the control involves only t f t0 c |g| 2 + |g t | 2 . In the papers [2,3,11] the control varies over a large portion of the boundary and in time, but the regularization terms used are essentially denote the primitive ow variables, where (t; x) is the density; v i (t; x) denotes the velocity in the x i -direction, i = 1; 2; v = (v 1 ; v 2 ) T ; and T (t; x) denotes the temperature. The pressure p and the total energy per unit mass E are given by…”

Section: Introductionmentioning

confidence: 99%

Optimal control of unsteady compressible viscous flows

Collis

Ghayour

Heinkenschloss

et al. 2002

Numerical Methods in Fluids

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SUMMARYThe control of complex, unsteady ows is a pacing technology for advances in uid mechanics. Recently, optimal control theory has become popular as a means of predicting best case controls that can guide the design of practical ow control systems. However, most of the prior work in this area has focused on incompressible ow which precludes many of the important physical ow phenomena that must be controlled in practice including the coupling of uid dynamics, acoustics, and heat transfer. This paper presents the formulation and numerical solution of a class of optimal boundary control problems governed by the unsteady two-dimensional compressible Navier-Stokes equations. Fundamental issues including the choice of the control space and the associated regularization term in the objective function, as well as issues in the gradient computation via the adjoint equation method are discussed. Numerical results are presented for a model problem consisting of two counter-rotating viscous vortices above an inÿnite wall which, due to the self-induced velocity ÿeld, propagate downward and interact with the wall. The wall boundary control is the temporal and spatial distribution of wall-normal velocity. Optimal controls for objective functions that target kinetic energy, heat transfer, and wall shear stress are presented along with the in uence of control regularization for each case.

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On the Use of LES with a Dynamic Subgrid-Scale Model for Optimal Control of Wall Bounded Turbulence

Cited by 7 publications

References 11 publications

Viscous effects in control of near-wall turbulence

Viscous effects in control of near-wall turbulence

A mathematical framework for multiscale science and engineering : the variational multiscale method and interscale transfer operators.

Optimal control of unsteady compressible viscous flows

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