Multi-block Poisson grid generator for cascade simulations

Groß, Andreas; Fasel, Hermann F.

doi:10.1016/j.matcom.2008.01.042

Cited by 60 publications

(7 citation statements)

References 16 publications

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“…The grid for the cascade itself was generated by means of a multi-block Poisson grid generator for cascade simulations (Gross and Fasel, 2008). The setup and domain decomposition of the linear low-pressure turbine with two wake generating bars is shown in figure 1.…”

Section: Methodsmentioning

confidence: 99%

The Influence Of Different Wake Profiles On Losses In A Low Pressure Turbine Cascade

Hammer¹,

Sandham²,

Sandberg³

2017

European Conference on Turbomachinery Fluid Dynamics and Hermodynamics

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Large eddy simulations were carried out in order to investigate the influence of unsteady wakes with different profiles on the loss mechanisms of the T106A linear LPT cascade. The upstream bars were set into rotation around their axis, thus generating circulation as well as asymmetrical wake profiles. Two different rotation rates were simulated yielding different wake parameters that were then compared to an actual turbine blade wake profile. It has been found that the commonly used non-rotating bars generated wakes several times stronger than that of a blade. Both wakes were able to trigger early boundary layer transition and thus intermittently prevent separation on the suction surface. However, the weaker wakes resulted in a larger and longer-lasting separation bubble. Interestingly, the bigger separation bubble, in combination with the weaker wakes, resulted in an overall lower loss.

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Section: Methodsmentioning

confidence: 99%

The Influence Of Different Wake Profiles On Losses In A Low Pressure Turbine Cascade

Hammer¹,

Sandham²,

Sandberg³

2017

European Conference on Turbomachinery Fluid Dynamics and Hermodynamics

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“…A Poisson grid generator was employed for obtaining O‐grids with high orthogonality and smoothness (Figure ). The free‐stream boundary was at a distance of 25 c from the center of the blade, where c is the chord length.…”

Section: Blade Section Simulationsmentioning

confidence: 59%

“…The local speed ratio at r = 10 was set to λ r = 6 such that the rotational speed of the rotor,

Ω MathClass-rel= \frac{u_{R}}{r \sqrt{1 MathClass-bin+ \frac{1}{λ_{r}^{2}}}} MathClass-rel= 0 MathClass-punc. 0986

was close to the corresponding number for the higher Reynolds number simulations of Sections 4.5 and 4.6. The resultant wind speed is

u_{1} MathClass-rel= \frac{u_{R}}{\sqrt{1 MathClass-bin+ λ_{r}^{2}}} MathClass-rel= 0 MathClass-punc. 1644

With

tan Φ MathClass-rel= \frac{u_{1}}{Ωr}

the local twist angle becomes

β MathClass-rel= Φ MathClass-bin- α

A Poisson grid generator was employed for generating a grid with 200 × 50 × 32 cells in the circumferential, wall normal and radial directions (Figure ). The grid extent in the wall normal direction was 2 c .…”

Section: Rotationmentioning

confidence: 99%

Numerical investigation of rotational augmentation for S822 wind turbine airfoil

et al. 2012

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Direct numerical simulations were carried out for an S822 wind turbine blade section at a chord Reynolds number of Re D 100;000 and an angle of attack of˛D 5°. Results for a stationary non-rotating blade section compare favorably with wind tunnel data by the University of Illinois at Urbana-Champaign and XFoil predictions. By adding volume forcing terms to the right-hand side of the Navier-Stokes equations, the Coriolis and centrifugal accelerations resulting from blade rotation are modeled in the blade section simulations. Blade rotation is shown to delay separation especially near the hub, resulting in a lift increase of up to 100% and a drag reduction. The simulations provide insight into a physical mechanism that offers an explanation for the lift increase observed for rotating blade sections when compared with stationary blade sections, which is commonly referred to as rotational augmentation. Rotation is shown to lead to a radial velocity component toward the blade tip in areas where the velocity is substantially different from its free-stream value, such as near the stagnation point and especially in the separated flow region, and to the appearance of stationary and traveling crossflow vortices. A linear stability theory analysis that compares favorably with the simulation data provides proof that the primary instabilities are of a mixed type, including both a two-dimensional mode (Tollmien-Schlichting and Kelvin-Helmholtz type) and a stationary and unsteady crossflow mode. The crossflow instabilities accelerate transition, leading to separation delay, lift increase and drag reduction. This effect is very pronounced at 20% blade radius and still present at 80% radius. Because periodicity conditions were applied in the spanwise direction, the present results provide an explanation for rotational augmentation that is not based on the transfer of fluid from the inboard region toward the blade tip ('centrifugal pumping'). For the low Reynolds number conditions considered here, crossflow instabilities, which destabilize the flow leading to earlier transition and a separation delay, may contribute to rotational augmentation.Accurate blade performance predictions are crucially needed during wind turbine development. A survey of numerical methods for analysing the aerodynamic and aeroelastic properties of wind turbines was provided by Hansen et al. 1 Blade element momentum theory (BEMT) is one of the main tools for computing the performance of entire turbines. This approach was found to perform well for attached flow conditions but underestimate the stall onset and hence the peak power output when two-dimensional (2D) airfoil data are used that are not corrected for rotational effects. For example, the National Renewable Energy Laboratory (NREL) Combined Experiment (Phase II) turbine power exceeded predictions by approximately 15-20%. 2 Almost all research suggests that this effect can be attributed to stall delay caused by blade rotation. 3The survey paper by Leishman et al. 4 points out that many details of the...

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“…The grid was generated with a multi-block Poisson grid generator for cascade simulations [26] and is similar to the one that was already used in the previously conducted LES study of Michelassi et al [11]. The setup and domain decomposition of the linear low-pressure turbine with two wake-generating bars is shown in Figure 1.…”

Section: Gridmentioning

confidence: 99%

The Influence of Different Wake Profiles on Losses in a Low Pressure Turbine Cascade

Hammer

Sandham

Sandberg

2018

IJTPP

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Large eddy simulations were carried out in order to investigate the influence of unsteady incoming wakes with different profiles on the loss mechanisms of the high lift T106Alinear low-pressure turbine (LPT) cascade. Bars placed upstream of the LPT blade were set into rotation around their axis, thus generating circulation, as well as asymmetrical wake profiles. Three different rotation rates were simulated, yielding different wake parameters that were then compared to an actual turbine blade wake profile. Whereas the commonly-used non-rotating bars generated wakes with turbulent kinetic energy levels several times higher than that of an actual blade wake, the case with counterclockwise rotation led to more rapid wake mixing. All three wakes were able to trigger boundary layer transition and thus intermittently prevent separation on the suction surface. However, the weaker the wakes, the larger and longer lasting the separation bubbles became, and an increase in profile losses could be observed. Interestingly, the configuration with the weakest wake and the largest separation bubble resulted in a reduction of the overall LPT loss.

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Multi-block Poisson grid generator for cascade simulations

Cited by 60 publications

References 16 publications

The Influence Of Different Wake Profiles On Losses In A Low Pressure Turbine Cascade

The Influence Of Different Wake Profiles On Losses In A Low Pressure Turbine Cascade

Numerical investigation of rotational augmentation for S822 wind turbine airfoil

The Influence of Different Wake Profiles on Losses in a Low Pressure Turbine Cascade

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