Role of Vortex Generators in Diffuser S-Ducts of Aircraft

Figure 1: Structured multi-block grid for an aircraft intake S-Duct with vortex generators.

2300 words / 11 minutes read

Highly offset modern-day S-ducts need flow control mechanisms like vortex generators or synthetic jets to control the flow. The general consensus among researchers is that RANS solvers are in-capable of meeting the challenges of S-duct internal flow fields, while DES is considered the most promising candidate. Lastly, structured grids are observed to provide superior flow field prediction for S-ducts on a fraction of a grid size than that needed for unstructured grids.

Introduction

The design of an optimized intake-duct is a trade-off between multiple requirements. This includes high-pressure recovery, low installation drag, low radar, and noise signatures, as well as minimum weight and cost. These requirements are driving the development of modern fighter aircraft and UAVs towards compact designs. This in turn has resulted in having a high degree of offset between the intake and engine face, leading to the design of highly bent S-ducts or serpentine ducts.

However, higher bend ducts lead to larger flow separation and vortex formation. With the compact nature of the duct, the available duct length is not good enough for the diffusion and dissipation of these secondary flows. This results in total pressure loss and higher flow non-uniformity at the engine fan face. This is something unpleasant, as flow distortion at the inlet can lead to reduced stability margin for the compressor or fan. Further, inlet distortion can cause high cycle fatigue resulting in increased maintenance costs, loss of aircraft operability, and even catastrophic loss of the aircraft.

Natural vortices in serpentine ducts. b. Vortices in a highly-bend modern-day S-duct.
Figure 2: a. Natural vortices in serpentine ducts. b. Vortices in a highly-bend modern-day S-duct. Image source Ref [7].
So, current industry research in S-duct is mainly focused on developing flow control systems to reduce and control flow distortion and flow losses while maintaining steady mass flow for optimal engine performance. Passive flow control using vortex generators and active flow control systems like synthetic jets are currently being studied.

CFD as a design tool is used extensively for accurate aerodynamic prediction of highly offset diffuser shapes. Attention is paid to understanding the variation in distortion levels and pressure recovery at the AIP with changes in duct shape. CFD is quite handy in studying the parametric variations of vortex generators and synthetic jets. Figure 3 shows the reduction in flow separation with the introduction of vortex generators.

Passive flow control by vortex generators

The effectiveness of the VGs depends on a number of parameters, but the main ones are the height of the VG, the orientation angle of the VG relative to the free stream and the number of VGs used. Hence testings’ are done by tweaking these parameters to study their influence on the flow.

Effect of Number of VGs: The placement of the VGs starts from the symmetry plane. The first one is placed near the symmetry plane and subsequent ones are placed in the circumferential direction at constant x. As can be observed in Figure 3, the flow quality improves with the usage of additional VGs in the form of diminishing of low Mach number region and the recovery of the pressure loss.

VG number effects - a. Mach contour on the symmetry plane. b. AIP. c. Separated area
Figure 3: VG number effects – a. Mach contour on the symmetry plane. b. AIP. c. Separated area. Image source Ref [2].
Effect of VG Height: The VG height has a decisive role in making VGs an effective flow control tool. The height of VGs is determined by the thickness of the local boundary layer and usually, a height of about 6 mm is considered as an optimal height. As can be observed in Figure 4c, the separated area decreases as the VG height increases from 3 mm to 6 mm, but when the height is made 9 mm, severe flow separation occurs.

VG height effects - a. Mach contour on the symmetry plane. b. AIP. c. Separated area.
Figure 4: VG height effects – a. Mach contour on the symmetry plane. b. AIP. c. Separated area. Image source Ref [2].
Effect of VG orientation: A typical orientation of VGs is around 18 degrees relative to the free stream direction. If the orientation is varied from 8 degrees to 28 degrees, it is observed that the contour plots for 8, 13, and 18 degrees are very similar, but a slightly more separated area is seen for the 8-degree configuration. For the 28 degrees case, the flow separation increases by a large magnitude.

VG orientation effects - a. Mach contour on the symmetry plane. b. AIP. c. Separated area
Figure 5: VG orientation effects – a. Mach contour on the symmetry plane. b. AIP. c. Separated area. Image source Ref [2].
Overall, for use of VGs help in improving the pressure recovery and reduction of distortion at the AIP. While the pressure recovery is only marginal at about 0.8%, the reduction in distortion is significant, reaching up to 45% for certain geometric variants of VGs.

Active flow control by synthetic Jets

Passive flow control techniques have been in existence for decades, but active flow control (AFC) methods like constant or pulsed air jets are a new budding research area. Here, the improvement in flow efficiency is accomplished through a delay in flow separation.

Figure 6 shows AFC configurations for constant blowing jets with three slit areas with different widths. Studies show that improvement in recovery and reduction of distortion happens with an increase in the mass flow rate of the jets. However, beyond a certain value, a further increase in mass flow rate makes the AFC less effective. Best performance is shown by the smaller area and higher jet velocity configuration. A maximum pressure recovery of 1.15% and distortion reduction by 60-80 percent have been reported.

Jet area effects - a. Mach contour on the symmetry plane. b. AIP. c. Separated area.
Figure 6: Jet area effects – a. Mach contour on the symmetry plane. b. AIP. c. Separated area. Image source Ref [2].
Overall, the AFC techniques are observed to achieve better pressure recovery and lower distortion compared to passive flow control techniques like VGs. In both methods, secondary flows at the first bend are reduced, while they fail to reduce the secondary flows effectively at the second bend.

The S-duct’s challenge to the CFD community

Accurate prediction of large separated flow regions such as seen in S-ducts by CFD is very difficult. Currently, efforts are put to evaluate and understand the capabilities and limitations of RANS, URANS, and DES methods in modeling the flow physics and performance characteristics of highly offset intake diffusers.

The general consensus among researchers is that RANS solvers are in-capable of meeting the challenges of S-duct internal flow fields. URANS shows better predictions capabilities by capturing the time-evolution of the flow. Further research and development are needed to make it more accurate. DNS or LES methods at least in principle are capable to make accurate predictions of the flow physics to the required level. However, being computationally expensive, they are still not feasible. Only DES – Detached Eddy Simulation, is considered among many researchers as the potential candidate for predicting unsteady flow fields, involving high levels of turbulence and occurrences of instantaneous flow phenomena.

DES, which is nothing but a hybrid of RANS-LES turbulence methods has computational costs which are quite acceptable. DES offers a nice balance by providing the physical accuracy of the LES with the cost-effectiveness of the RANS. Since building a DES code involves, only coupling the pre-existing RANS and LES code, they reduce the time and cost needed for development.

Wall streamlines from the wall shear stress distributions for a. RANS b. X-LES-medium simulations
Figure 7: Wall streamlines from the wall shear stress distributions for a. RANS b. X-LES-medium simulations. Image source Ref [4].
The RANS and the LES inside a DES code: In standard DES, RANS is used to treat the boundary layer where the turbulent length scales are smaller, while LES is applied in regions with more uniform properties, larger turbulent length scales, and in locations where more physical relevance is required. Provision for implicit switching between RANS and LES is made depending on the RANS length scale and LES filter width.

This bifurcation of the regions is essential because there are major differences between RANS and LES arising due to the different turbulence modeling approaches. LES resolves the larger scales of the flow and models only the smaller scales. While, RANS on the other hand does not explicitly resolve any scales, but calculates the mean flow quantities and models the turbulent scales.

Since in LES, the flow turbulent scales are explicitly resolved, the generated eddy viscosity is smaller compared to that in RANS. As a result, LES reduces the viscous dissipation and diffusion in the flow, thereby allowing weaker flow structures to sustain in the solution.

Another difference between RANS and LES is the grid dependency. In RANS, the turbulence model is based on flow quantities and is similar for every grid, while in LES, the filter width is directly dependent on the grid-spacing. What this means is that any grid refinement in LES not only influences the numerical accuracy, but also the subgrid-scale turbulence model. As a consequence, unlike in RANS, grid-refinement beyond a point of convergence in LES will not produce the same solution.

When we consider standard DES in general, it can be noticed that there is a large dependency on the RANS part of the simulation, which requires a tangential grid spacing on the wall to be greater than the local boundary layer thickness. Sticking to this gridding requirement may be very difficult inside the duct. In such circumstances, if the switching to LES occurs inside the RANS boundary layer, then there will be an underestimation of the skin friction coefficient.

So, in order to overcome this grid-induced separation, newer approaches like DDES – Delayed Detached Eddy Simulation and ZDES – Zonal Detached Eddy Simulation have been developed. In DDES, the switch to LES mode is delayed while in ZDES, the RANS and DES zones are selected individually, to have clarity of the role of each region. These steps are undertaken to avoid ‘model stress depletion’ and grid-induced flow separation.

Comparison of diffuser flow solutions  a. time-averaged.  b. instantaneous flow field. Schlieren-like visualization with different physical time steps applied
Figure 8: Comparison of diffuser flow solutions  a. time-averaged.  b. instantaneous flow field. Schlieren-like visualization with different physical time steps applied. Image source Ref [4].
To a good extent, the global flow features are correctly captured by RANS and time-averaged DES. The computed total pressure loss and Mach number at the engine face agree well with the experimental results. When compared to experimental results, Mach number from RANS differs by about 9% while URANS differs by 5%.

In RANS alone, the predicted flow distortion and pressure recovery depend highly on turbulence modeling. Depending on the turbulence model chosen, the total pressure recovery is reported to vary from 0.1% to -1.8%, while flow distortion is observed to differ widely from +37% to +126%.
However, many major differences exist between the numerical results and experiments. The region with total pressure losses doesn’t conform with the experiments. The distortion parameter is systematically overestimated, with the DES solution differing from experiments by a larger value than RANS, as DES overestimates the separated flow region. Also, DES shows a delay in the development of the instabilities in the shear layer.

Grids for S-duct. a. Structured. b. Unstructured.
Figure 9: Grids for S-duct. a. Structured. b. Unstructured. Image source Ref [1].

Which grid type is better for S-duct?

Just like the need to pick the right solver type, there is a need to pick the right grid type to do accurate CFD prediction for S-ducts. Studies have shown that grid generation both in structured and unstructured ways for S-ducts is quick, efficient, and reliable. However, structured meshes tend to achieve superior performances than high-quality unstructured meshes says a research study by ANSA, BETA CAE system.

Surface mesh for an S-duct without vortex generators using GridPro
Figure 10: Surface mesh for an S-duct without vortex generators using GridPro.

Solution differences between the structured medium grid and fine grid were observed to be very small. Even though the grid size nearly doubles in between the medium and fine grids, the AIP back pressure was seen to make only a marginal variation of 0.05% between the two grids. On the other hand, notable differences were noticed between the unstructured medium grid and fine grid simulation results. The difference in predicted backpressure between the two grids is about 1%.

Structured multi-block grid for an S-duct with vortex generators using GridPro
Figure 11: Structured multi-block grid for an S-duct with vortex generators using GridPro.

When the flow field as generated by equivalently resolved unstructured and structured grids was compared, the differences were significant. To make the grids more relatable to each other, the fundamental cell size was kept the same for the two different mesh setups. Tetrahedral cells being isotropic in nature needs more mesh cells to achieve a roughly equivalent sized structured grid cell.

Nested multi-block grid for an S-duct with vortex generators using GridPro
Figure 12: Nested multi-block grid for an S-duct with vortex generators using GridPro.

Thus, the structured mesh outperforms the unstructured hybrid grid despite having a far lower cell count. The structured grid with a cell count of 4.2 million predicts a more greatly resolved flow solution than its equivalent unstructured counterpart. Interestingly, to achieve a similarly resolved flow solution, the unstructured approach demanded a grid of size 31.2 million.

Nested grids around the vortex generators using GridPro
Figure 13: Nested grids around the vortex generators using GridPro.

What we can conclude from these gridding experiments is that, at least for flows in S-duct with significant shear, an order of magnitude more unstructured cells are needed to match an equivalent structured grid in terms of solution accuracy and flow field resolution. Even with VGs, structured meshes require far fewer cells. Thus, structured meshes require lesser computational resources to predict higher-quality flow fields than higher-density unstructured grids.

Structured grids and unstructured grids around vortex generators
Figure 14: Structured grids and unstructured grids around vortex generators. Image source Ref [1].

Parting Remarks

This brings us to the end of this article. Complete elimination of flow distortions in S-duct at all flight conditions is impossible. The use of flow control mechanisms like vortex generators or synthetic jets is the way to go forward in dealing with flow separations.

Evaluation of the distortion parameter results from steady and dynamic simulations at the engine face reveal that only dynamic simulations can provide the correct assessment of the performance parameters considering the distortion limits as required by the engine manufacturers. Compared to RANS, DES results, in general, are in accordance with the experiments, but in the future, its capabilities need to be further enhanced to improve its accuracy to the level required by industrial standards.

Physical time step size and grid resolution have an important role in the outcome of the computational results. For DES simulations, a strong association exists between the grid and the ability of the algorithm to correctly manage the varying turbulent scales.

Structured meshes are reported to provide superior solutions compared to unstructured meshes. Needing only a fraction of the cell count as needed by unstructured meshes, structured grids are observed to provide a better-resolved flow field and tend to take far lesser computational resources.

Further Reading

  1. Aircraft Vortex Generators – The Nacelle Strakes
  2. Engine Nacelle Aerodynamics
  3. A Whale of an Idea in Wing Design

References:

1. “Numerical Simulations of Flow Through an S-Duct”, Pravin Peddiraju, Arthur Papadopoulos, Vangelis Skaperdas, Linda Hedges, BETA CAE Systems, 6th BETA CAE International Conference.

2. “CFD Simulation of Serpentine S-Duct with Flow Control”, Lie-Mine Gea, 51st AIAA/SAE/ASEE Joint Propulsion Conference, July 27-29, 2015, Orlando, FL.

3. “CFD Validation and Flow Control of RAE-M2129 S-Duct Diffuser Using CREATE-AV Kestrel Simulation Tools”, Pooneh Aref et al, Aerospace 2018, 5, 31.

4. “Numerical simulations for high offset intake diffuser flows”, T.M. Berens et al, NLR-TP-2014-096.

5. “A Multi-objective shape optimization of an S-Duct intake through NSGA-II genetic algorithm”, Aurora Rigobello, 5 Dicembre 2016, Universita’ Degli Studi Di Padova.

6. “S-Duct Inlet Design for a Highly Maneuverable Unmanned Aircraft”, Jacob Brandon, Thesis, The Ohio State University, 2020.

7. “Effectiveness of a Serpentine Inlet Duct Flow Control Scheme at Design and Off-Design Simulated Flight Conditions”, Angela C. Rabe, Doctor Of Philosophy In Mechanical Engineering, Virginia Polytechnic Institute and State University, August 1, 2003.

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