RANS and LES/RANS Simulation of Airfoils under Static and Dynamic Stall

Ke, Jianghua; Edwards, Jack R.

doi:10.2514/6.2013-955

Cited by 5 publications

(15 citation statements)

References 15 publications

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“…10 CFD (Computational Fluid Dynamics) results may show a single rotating structure, where experimental results will reveal a richer mosaic of smaller (occasionally larger) coherent structures. Thus, it will appear that there is significant variation between CFD results and measured results when the angle of attack enters into deep stall conditions, [11][12][13] but this may be a reflection of the nature by which the CFD calculations are performed. 14 In order to control dynamic stall (i.e., load variations are mitigated, or stall is delayed to higher angles of attack), Aerodynamic Flow Control (AFC) has been investigated as a means to accomplishing such a control.…”

Section: Introductionmentioning

confidence: 83%

Dynamic stall process on a finite span model and its control via synthetic jet actuators

Taylor

Amitay

2015

Physics of Fluids

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An experimental study of the process by which dynamic stall occurs on a finite span S809 airfoil was conducted at the Center for Flow Physics and Control at Rensselaer Polytechnic Institute. Understanding the flow field around a dynamically pitching airfoil helped in controlling the dynamic stall process through active flow control via synthetic jet actuators. The three component, two dimensional flow fields were measured with a stereoscopic particle image velocimetry system. This study demonstrated that, through the introduction of periodic momentum near the leading edge of this model, the evolution of the dynamic stall vortex, which forms and convects downstream under dynamic conditions, could be delayed or suppressed in favor of the preservation of a trailing edge vortex that arises due to trailing edge separation and recirculation in the time averaged sense. This process seems to be the result of changing how the flow field transitions from trailing edge separation to a fully separated flow. In a phase-averaged sense, absent of flow control, this process is defined by the creation of a phase averaged leading edge recirculation region, which interacts with the trailing edge separation. Through the introduction of momentum near the leading edge, this process can be altered, such that the phase averaged trailing edge separation region is the dominant structure present in the flow. Additionally, a cursory investigation into the instantaneous flow fields was conducted, and a comparison between the phase averaged flow field and instantaneous fields demonstrated that while similar effects can be observed, there is a significant difference in the flow field observed in the instantaneous fields versus the phase averaged sense. This would imply that a different method of analyzing dynamic stall from PIV measurements may be necessary.

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Section: Introductionmentioning

confidence: 83%

Dynamic stall process on a finite span model and its control via synthetic jet actuators

Taylor

Amitay

2015

Physics of Fluids

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“…Each hybrid LES/RANS model is implemented into a finite volume Navier-Stokes solver that solves a low Mach number form of the Navier-Stokes equations, in which the governing equations are formulated in an arbitrary Lagrangian-Eulerian (ALE) fashion to enable moving-mesh flow simulations in dynamic stall case [28]. Artificial compressibility (AC) method is used to solve the PDE system of the incompressible flow by introducing a fake time derivative of pressure in the continuity equation [28,30]; and the primitive variable vector ⃗ [ ] is used in the reconstruction.…”

Section: Hybrid Les/rans Models and Numerical Methodsmentioning

confidence: 99%

“…Inviscid fluxes are discretized using a variant of the piecewise parabolic method (PPM) [29] along with a low Mach number extension of Edwards's low-diffusion flux-splitting scheme [28,30], whereas viscous and diffusive fluxes are discretized using second-order central differences. An implicit artificial compressibility method combined with sub-iteration procedure is employed to obtain second-order temporal accuracy [28,30]. Table 1 shows the characteristics of the C-type meshes used in the RANS and hybrid LES/RANS simulations in this study.…”

Section: Hybrid Les/rans Models and Numerical Methodsmentioning

confidence: 99%

“…Artificial compressibility (AC) method is used to solve the PDE system of the incompressible flow by introducing a fake time derivative of pressure in the continuity equation [28,30]; and the primitive variable vector ⃗ [ ] is used in the reconstruction. Inviscid fluxes are discretized using a variant of the piecewise parabolic method (PPM) [29] along with a low Mach number extension of Edwards's low-diffusion flux-splitting scheme [28,30], whereas viscous and diffusive fluxes are discretized using second-order central differences. An implicit artificial compressibility method combined with sub-iteration procedure is employed to obtain second-order temporal accuracy [28,30].…”

Section: Hybrid Les/rans Models and Numerical Methodsmentioning

confidence: 99%

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RANS and LES/RANS Simulations of Flow Over an Dynamically Pitching Naca0012 Airfoil

Edwards

2015

53rd AIAA Aerospace Sciences Meeting

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The hybrid Large-Eddy/Reynolds-Averaged Navier-Stokes (LES/RANS) and RANS simulations are used to investigate the properties of subsonic flow over NACA 0012 airfoils undergoing static and dynamic stall conditions (M=0.1, α=16.7°, Re c =1.0×10 6 ). In the simulations of NACA 0012 airfoil at static stall case, Menter's SST model predicts an attached flow at the leading edge, whereas the Gieseking's LES/RANS model on a coarser mesh predicts a massively separated flow characterized by the stabilization of a detached leading edge vortex near the trailing edge. The predictions by Gieseking's model on a coarse mesh agree closely with PIV measurements of mean velocity, the Reynolds axial stress and the Reynolds normal stress, but over-predict the magnitude of the Reynolds shear stress. However, Gieseking's model on a fine mesh predicts a more attached flow because the under-resolved LES on the fine mesh (but not fine enough as required in a wall-resolved LES) fails to reproduce the cascade process at the smaller scale and results in an overlyenergetic boundary layer near leading edge which resists and delays the separation. In 3D simulations of dynamic stall case where the NACA 0012 airfoil is dynamically pitching about its quarter-chord at a reduced frequency, k r =0.1, Gieseking's model on a coarse mesh in spanwise direction correctly predicts response of the massive separation at static stall angle of 16.7° during downstroke pitching, but it also predicts some leading edge separation which is not present in the experiment during upstroke pitching. Mesh refinement in the spanwise direction helps reducing the level of leading edge separation during upstroke pitching, but results in an under-separated flow solution for downstroke response, which is consistent with what happens in the static stall case and the reason is also similar. By introducing a grid correction function in the definition of outer layer turbulence length scale, Salazar's fix takes grid resolution into consideration while determining the RANS to LES transition. The inclusion of Salazar's fix to Gieseking's model delays the leading edge separation during upstroke pitching, but it also introduces a too-attached flow for downstroke response. Nomenclature c = airfoil chord C N = model constant for blending function in Gieseking's model C s = model constant (sharpening factor) in Gieseking's model  C = model constant in Menter SST turbulence f = pitching frequency k = turbulent kinetic energy k r = reduced frequency L = representative length scale l inner = inner layer turbulent length scale l outer = outer layer turbulent length scale M = Mach number = freestream pressure Re c = chord Reynolds number S = magnitude of strain rateAIAA SciTech T = temperature u, v, w = velocity in x, y, z directions = freestream velocity x/c = axial distance over axial chord = angle of attack = blending function for LES/RANS eddy viscosity = density = ratio of turbulence length scale = molecular viscosity = eddy viscosity = specific turbulence dissipation rate = magnitude of...

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“…As Gieseking's model is dependent on both inner-layer and outerlayer length-scale information, it is capable of adjusting to strong departures from local equilibrium and can respond to large changes in the boundary-layer thickness without problem-specific adjustment. The model has been used in simulations of shock / boundary layer interactions [19,20], scramjet combustor calculations [21][22][23], and airfoils undergoing static stall [24] and dynamic stall [25,26].…”

Section: A Les/rans Hybridizationmentioning

confidence: 99%

Development of a new LES/RANS formulation

Shen

Edwards

2016

46th AIAA Fluid Dynamics Conference

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A new large-eddy simulation / Reynolds-averaged Navier-Stokes (LES/RANS) turbulence model is described in this work. In common with other such models developed at NCSU, the RANS-to-LES transition is facilitated by a flow-dependent blending function based on estimates of outer-and inner turbulence length scales. In contrast to prior work, the outer scale is a function of an eddy viscosity specifically used for this purpose. A transport equation for this eddy viscosity is solved, with excessive growth of eddy viscosity due to fluctuating strain rates controlled by a destruction term based on the von Kármán length scale. The new model is completely local and is capable of adjusting to the changing state of a boundary layer. The model is tested for incompressible and compressible flat-plate boundary layers at low and moderate Reynolds numbers and for flow over an airfoil with trailing-edge separation. The model is shown to be capable of predicting the composite structure of a boundary layer with minimal log-law mismatch.The model also possesses additional degrees of freedom that may enable improved predictions for strongly nonequilibrium turbulent flows.

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RANS and LES/RANS Simulation of Airfoils under Static and Dynamic Stall

Cited by 5 publications

References 15 publications

Dynamic stall process on a finite span model and its control via synthetic jet actuators

Dynamic stall process on a finite span model and its control via synthetic jet actuators

RANS and LES/RANS Simulations of Flow Over an Dynamically Pitching Naca0012 Airfoil

Development of a new LES/RANS formulation

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