Large‐eddy simulation of oscillating boundary layers: Model comparison and validation

Radhakrishnan, Senthil K.; Piomelli, Ugo

doi:10.1029/2007jc004518

Cited by 29 publications

(33 citation statements)

References 58 publications

(116 reference statements)

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“…During the last decade, large eddy simulation (LES) has been proved able to simulate accurately fully developed turbulent flows [11,[28][29][30][31]. In their work, Lohmann et al [30] used the classical Smagorinsky subgrid model to investigate a ventilated Stokes boundary layer in the turbulent regime at Re δ = 3464 and obtained overall reasonable agreement with experimental data of Jensen et al [8].…”

Section: Introductionsupporting

confidence: 63%

Large eddy simulation of boundary layer flow under cnoidal waves

Chen

Zhou

et al. 2015

Acta Mech. Sin.

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Water waves in coastal areas are generally nonlinear, exhibiting asymmetric velocity profiles with different amplitudes of crest and trough. The behaviors of the boundary layer under asymmetric waves are of great significance for sediment transport in natural circumstances. While previous studies have mainly focused on linear or symmetric waves, asymmetric wave-induced flows remain unclear, particularly in the flow regime with high Reynolds numbers. Taking cnoidal wave as a typical example of asymmetric waves, we propose to use an infinite immersed plate oscillating cnoidally in its own plane in quiescent water to simulate asymmetric wave boundary layer. A large eddy simulation approach with Smagorinsky subgrid model is adopted to investigate the flow characteristics of the boundary layer. It is verified that the model well reproduces experimental and theoretical results. Then a series of numerical experiments are carried out to study the boundary layer beneath cnoidal waves from laminar to fully developed turbulent regimes at high Reynolds numbers, larger than ever studied before. Results of velocity profile, wall shear stress, friction coefficient, phase lead between velocity and wall shear stress, and the boundary layer thickness are obtained. The dependencies of these boundary layer properties on the asymmetric degree and Reynolds number are discussed in detail.

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Section: Introductionsupporting

confidence: 63%

Large eddy simulation of boundary layer flow under cnoidal waves

Chen

Zhou

et al. 2015

Acta Mech. Sin.

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“…The LES model has been validated by Silva Lopes and Palma [2002] in isotropic turbulence, by Silva Lopes et al [2006] in an S‐shaped duct, and by Radhakrishnan et al [2006] for nonequilibrium flows. In particular, Radhakrishnan and Piomelli [2008] have validated the model against the laboratory experiments of Jensen et al [1989] for oscillating boundary layers. In their numerical experiments, Radhakrishnan and Piomelli [2008] explored a number of subgrid scale and wall‐layer models.…”

Section: Introductionmentioning

confidence: 99%

Large‐eddy simulation of the tidal‐cycle variations of an estuarine boundary layer

Li¹,

Radhakrishnan²,

Piomelli³

et al. 2010

J. Geophys. Res.

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[1] The estuarine boundary layer affected by a horizontal density gradient exhibits temporal evolution over a tidal cycle, in a manner similar to the diurnal cycle of the ocean surface mixed layer. A large eddy simulation (LES) model is developed to investigate the physics controlling the growth of the boundary layer during the flood tide and restratification during the ebb tide. Turbulent kinetic energy, momentum and salt fluxes, bottom stress, and energy dissipation rates calculated from the LES model all show a strong flood-ebb asymmetry. Analysis of the turbulent kinetic energy (TKE) budget shows a primary balance between shear production and dissipation in the well-mixed boundary layer over the tidal cycle. However, TKE transport term is found to be important across the edge of the boundary layer during the flood tide so turbulent energy generated in the bottom boundary layer can be transferred to the stratified pycnocline region. Tidal straining leads to a small and weakly convective region inside the boundary layer during the flood tide but the strain-induced buoyancy flux does not make a significant contribution to the turbulence generation. Additional LES runs are conducted by switching off the baroclinic pressure gradient term in the momentum equation and the tidal straining term in the salinity equation to show that the baroclinic pressure gradient is the main mechanism responsible for generating the flood-ebb mixing asymmetry.

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“…4,5 To overcome this deficiency, scale dependent dynamic models have been formulated [5][6][7][8][9] and are beginning to be implemented for various applications. [10][11][12][13] As with the traditional dynamic models, the smallest resolved scales are used to obtain the model coefficient. However, scale dependent formulations also interrogate the smallest resolved scale about the variation of the model coefficient with filter scale.…”

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confidence: 99%

Scale dependence of subgrid-scale model coefficients: An a priori study

et al. 2008

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Dynamic subgrid-scale models require an a priori assumption about the variation in the model coefficients with filter scale. The standard dynamic model assumes independence of scale while the scale dependent model assumes power-law dependence. In this paper, we use field experimental data to investigate the dependence of model coefficients on filter scale for the Smagorinsky and the nonlinear models. The results indicate that the assumption of a power-law dependence, which is often used in scale dependent dynamic models, holds very well for the Smagorinsky model. For the nonlinear model, the power-law assumption seems less robust but still adequate. © 2008 American Institute of Physics. ͓DOI: 10.1063/1.2992192͔ I. BACKGROUNDThe dynamic approach introduced by Germano et al. 1represents a significant milestone in the development of generalized subgrid scale ͑SGS͒ turbulence models for large eddy simulations ͑LES͒. The approach computes an optimal model coefficient based on information from the smallest resolved scales in a simulation using the Germano identity. Although the approach was formulated for the Smagorinsky model, 2 the Germano identity can be applied to other SGS models as well ͑see, for example, Armenio and Piomelli 3 ͒. The traditional dynamic approach makes the assumption of scale invariance, i.e., that the coefficients do not depend on filter scale. The same coefficients determined from the smallest resolved scale are used for the SGS. This scale invariance assumption has been found to break down under various conditions where the filter cutoff scale falls near a transition scale rather than in the inertial subrange. 4,5 To overcome this deficiency, scale dependent dynamic models have been formulated [5][6][7][8][9] and are beginning to be implemented for various applications. [10][11][12][13] As with the traditional dynamic models, the smallest resolved scales are used to obtain the model coefficient. However, scale dependent formulations also interrogate the smallest resolved scale about the variation of the model coefficient with filter scale. This is done by using two test filtering operations that yield the coefficient values at two resolved scales ͑the classic dynamic approach uses only one test filter scale͒. The information at the two test filter scales is then extrapolated to compute an optimal model coefficient that applies to the unresolved scales.An assumption has to be made in the scale dependent formulations regarding the functional dependence of the SGS model coefficients on scale. A power-law functional dependence has been used in all previous scale dependent model implementations. Previously reported a priori tests of the scale dependent model 14 already suggested that it can accurately predict optimal Smagorinsky model coefficients for the velocity field ͑as determined by matching measured and modeled SGS TKE dissipations͒, while the scale invariant formulation underpredicted the coefficients. In addition, a posteriori tests also show that simulations with scale dependent dynamic m...

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Large‐eddy simulation of oscillating boundary layers: Model comparison and validation

Cited by 29 publications

References 58 publications

Large eddy simulation of boundary layer flow under cnoidal waves

Large eddy simulation of boundary layer flow under cnoidal waves

Large‐eddy simulation of the tidal‐cycle variations of an estuarine boundary layer

Scale dependence of subgrid-scale model coefficients: An a priori study

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