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Computational Assessment of the HiLiftPW-1 Trap-Wing Model Using the elsA CFD Software

Ludovic Wiart1 and Mickaël Meunier1 ONERA, Applied Aerodynamics Department, Meudon, 92190, France

This paper exhibits the computational studies carried out at ONERA in the frame of the first High Lift Prediction Workshop, using the ONERA-elsA solver in RANS mode. The selected configuration for this workshop is the NASA Trap-Wing model, representative of a swept, medium/high-aspect ratio wing in landing/take-off configuration. First, grid refinement and flap deflection effects are investigated on coincident multiblock structured grids provided by the organizing committee. Then additional studies, such as flap support effects, are presented on in-house overset grids. Results of post-processing using the ffd72 far-field extraction tool on this configuration are also presented. The elsA computations are in very good agreement with the experimental data available for the Trap-Wing model.

Nomenclature AoA = angle of attack CD = drag coefficient CL = lift coefficient CLmax = maximum lift coefficient Cm = pitching moment coefficient Cp = pressure coefficient CDnf = near-field drag coefficient CDv = viscous drag coefficient CDvp = viscous pressure drag coefficient CDw = wave drag coefficient CDi = induced drag coefficient c = mean aerodynamic chord

= angle of attack max = angle of attack corresponding to CLmax = spanwise location as a fraction of half span

I. Introduction ODAY CFD solvers have the capability to predict with accuracy the forces and moments applied on the aircraft around the cruise conditions, as demonstrated by the Drag Prediction Workshop series (Ref. 1). Hence the will

to investigate their behavior in different (and more challenging) flight conditions such as take-off or landing. Therefore the first High Lift Prediction Workshop was initiated by a working group of AIAA Applied Aerodynamics Technical Committee members. The geometry selected for this benchmarking is the NASA Trap-Wing model, representative of a swept, medium/high-aspect ratio wing in landing/take-off configuration.

In order to enhance its knowledge concerning the CFD prediction capability for high-lift flow fields, the Civil Aircraft Unit of the Applied Aerodynamics Department of ONERA participated in the HiLiftPW-1. Preliminary ONERA results obtained with the elsA (Ref. 2,3) software were shown during the workshop held in Chicago, Illinois, in June 2010. This paper presents more detailed analysis of these results and additional studies that have been carried out since then. Experimental data for the NASA Trap-Wing model were available from the beginning and are therefore used for comparison with our computational results.

1 Research engineer, Applied Aerodynamics Department, ONERA, 8 rue des Vertugadins, 92190 Meudon, France

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49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition4 - 7 January 2011, Orlando, Florida

AIAA 2011-865

Copyright © 2011 by L. Wiart and M. Meunier. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

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After a brief presentation of the configuration, the grids and the elsA aerodynamic software, this paper proposes grid convergence and flap deflection studies carried out on coincident multiblock structured grids. Computations performed using the Chimera approach implemented in elsA are then presented, including a study of flap support effects.

II. NASA Trap-Wing model, grids and aerodynamic software

A. NASA Trap-Wing model The selected test case is the so-called NASA “Trap-Wing” model which has been extensively submitted to wind

tunnel testing in NASA LaRC 14x22 wind tunnel and NASA ARC 12-Foot pressurised wind tunnel since 1998. Its geometrical characteristics (see Figure 2) are given below:

half span = 2.12 m; mean aerodynamic chord (MAC) = 1 m; aspect ratio = 4.56; leading edge sweep = 33.9°; quarter chord sweep = 30.0°; taper ratio = 0.4.

The Trap-Wing model provides a “simple” geometry with the flow features relevant to high-lift flow fields (see

Figure 1) and presents computational challenges for: massive separations (flap/side-of-body, flap trailing edge, slat tip); unsteady effects; strong streamline curvature; transition (wall bounded and free shear layers).

Three different configurations were submitted for study:

“Config 1” – Slat 30°, Flap 25° (landing configuration) “Config 8” – Slat 30°, Flap 20° (take-off configuration) “Config 1” with slat and flap brackets

B. Structured grids used 1) Committee supplied grids The multiblock structured grids supplied by the High Lift Prediction Workshop committee that we used to

comply with the workshop requirements were created by Boeing (Str-OnetoOne-A-v1). They do not include the slat and flap brackets. The three grid levels we computed are:

Coarse: 22.5x106 nodes; Medium: 52x106 nodes; Fine: 170.5x106 nodes.

This meshing strategy insures precise control on grid quality near the surfaces (see Figure 3). However, besides

the real effort required to mesh that kind of high lift configuration using coincident structured grids, the topology would have to be revised in case of a change in geometry, such as the integration of brackets for instance.

Figure 1 – CFD challenging flow features Figure 2 – The Trap-Wing model in NASA 12-Foot pressurised wind tunnel

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2) In-house overset grids In order to overcome that difficulty, we decided to use the elsA Chimera features to mesh independently the

wing/fuselage, the slat and the flap. A structured overset grid was therefore created at ONERA using the Icem-CFD software. This grid has 32.5x106 nodes and is equivalent to the supplied Medium grid in the way that it uses the same edge node distributions, wherever possible. The difference in the number of mesh nodes between coincident and overset grids mainly comes from the slat and flap wake meshing that are not propagated downstream in the overset case. The fuselage and the wing constitute the background mesh, while the slat and the flap were meshed apart, allowing more flexibility in edge point distribution.

The grid assembly was performed using a cartesian mask based method (see Figure 4). The flap mesh below the wing trailing edge is masked in order to avoid degradation of the wing wake by overset interpolations. Concerning the interpolations between two grids, we used four overlapping cells (2 from each grid) in order to insure good data transmission.

We also used the flexibility of this mesh generation method to include the flap brackets (see Figure 4). The slat

brackets are a lot smaller and were thus considered to have little effect on the global aerodynamic coefficients. We included the flap brackets on the previous grid using the same assembly method as presented above. The resulting grid has 63x106 nodes, their amount increasing not only due to the brackets grids themselves but also because of the refinement of the background mesh in the spanwise direction that was necessary in the vicinity of the brackets to ensure proper overset interpolations.

Figure 4 – Overset grids assemblies, without brackets (left) and with flap brackets (right), at =70%

C. CFD software: ONERA-elsA and ONERA-ffd72 The calculations presented in this paper are performed with the ONERA elsA code. This software uses a cell-

centered finite-volume discretization on structured multiblock meshes. Time integration is carried out by a backward-euler scheme with implicit LU-SSOR relaxation. Spatial discretization is realized through a central Jameson scheme with artificial viscosity. V-cycle multigrid technique and low-speed preconditioning are used to accelerate the convergence. All the computations are performed in fully turbulent conditions.

The far-field drag extraction software ffd72 (Ref 4,5,6) is used to provide a physical drag breakdown into

viscous, wave and lift-induced drags and to eliminate spurious drag by difference with the sum of pressure and friction drag coefficients.

Figure 3 – Structured mesh topology detail at =70%

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farfieldnearfieldsp

iwvfarfield

vpfv

pfnearfield

CDCDCD

CDCDCDCD

CDCDCD

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This tool also allows to identify the different regions of drag productions (see III.G), which is very useful for

design purpose for instance.

III. Grid convergence and flap deflection studies This study corresponds to Test Case 1 of the first High Lift Prediction Workshop and concerns the “Config 1” of

the Trap-Wing. The aerodynamic conditions are the following: Mach number: M=0.2; Reynolds number based on mean aerodynamic chord: Re=4.3x106.

A. Computation strategy The polars were obtained with the elsA software the same way the experimental ones were obtained, meaning

with increasing AoA. These first calculations were carried out using the Spalart-Allmaras turbulence model. Although decreasing AoA polars were not calculated, some computations carried out without initialization from

previous AoA showed the non uniqueness of the flow solution at high AoA. However, decreasing AoA polars were shown by the HiLiftPW committee during the Chicago workshop (see Figure 5) showing that hysteresis is more limited in terms of AoA range than originally expected from the computational results (from -1 to +1° and from 33 to 37°). Thus hysteresis can not be directly related to the divergence between calculations from scratch and restarting computations, which occurs as early as 21° (see Figure 5). Some workshop participants raised the possibility that this early stall behavior could be cancelled when performing time accurate calculations.

Figure 5 – Non uniqueness of the numerical flow solution (left) and experimental evidence of hysteresis (right) for Config 1

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D. Effects of different turbulence models Different turbulence models have been tested on this configuration, with one (Spalart-Allmaras with or without

rotation correction) or two (SST versions of k- models) transport equation(s). We noticed for this configuration that the SST models tend to overestimate the size of the flap trailing edge separation (see Figure 6), thus predicting too low lift levels, the gap with experimental data increasing with AoA.

Figure 6 – Effect of turbulence model on skin pressure distribution (Config 1, =13°)

Figure 7 – Effect of rotation correction in SA computations (Config 1)

Figure 7 shows how the rotation correction impacts the computational stall behavior. Since the main differences

between SA and SA_RC (SA with rotation correction) calculations were observed on the skin pressure distribution at the most outboard stations, we decided to investigate this flow area, where a slat-tip vortex is active.

If this latter is badly resolved, it can impact the predicted max and CLmax. The vortex can provoke flow separation by sucking up the boundary layer of the wing upper surface (see Figure 8). The experimental pressure data show that for that angle of attack, the wing experiences no separation at the tip. This could explain the early stall predicted with SA_RC.

Figure 8 – Impact of slat-tip vortex resolution on stall using SA (left) or SA_RC (right) (Config 1, =32°)

Isosurfaces of vorticity

Friction lines

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For all these reasons, all the calculations presented in the rest of this paper use the Spalart-Allmaras (SA) turbulence model, without rotation correction. However, independently from the selected turbulence model, the provided grids have no specific refinement to capture this vortex accurately.

E. Grid convergence study The three available multiblock Boeing grids presented earlier are used in this study. Figure 9a) shows a

comparison of lift and total drag coefficients convergence obtained at an AoA of 13°. Around 6000 iterations are necessary to achieve convergence at that AoA.

A comparison of the global coefficients obtained on the three different grid levels at AoA of 13 and 28° are presented in Figure 9b). Although no asymptotic convergence is reached as such, the coefficient variations between the different grid levels are small: - at most 10 drag counts, 2x10-3 on lift and 1x10-3 on pitching moment for =13°; - at most 60 drag counts, 3x10-3 on lift and 4x10-3 on pitching moment for =28°.

Some skin pressure distribution plots are shown in Figure 9c) and Figure 9d). They are taken from areas supposed to be very sensitive to grid refinement, such as the wing tip and the flap leading edge, but few discrepancies are visible between the different grid levels.

In these conditions, it could mean that the grid convergence is almost achieved from the coarse grid level using elsA.

a) Convergence of lift and drag coefficients ( =13°)

b) Grid convergence

c) Skin pressure distribution in the flow direction

( =28°)

d) Spanwise skin pressure distribution ( =28°)

Figure 9 – Influence of grid refinement (Config 1)

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F. Flap/Side-of-body separation This wind tunnel model presents a separation bubble at the junction of the fuselage and the flap. This

unstationnary phenomenon is known to be very difficult to capture using RANS equations (Ref. 7). Its sensitivity to different parameters has been investigated:

1) Grid refinement effects The change of grid level has very little influence on the separation bubble topology. This is an indication of good grid convergence.

2) Influence of angle of attack Increasing the AoA leads to a better flow supply for the flap, thus reducing the separation bubble size. 3) Influence of flap deflection angle The effect of decreasing the flap deflection angle is similar to an increase of AoA, but for a different reason. While the flap load decreases, the adverse pressure intensity decreases too, leading to a smaller separation zone.

G. Far-field analysis A far-field drag analysis has been carried out on the Boeing supplied grids using the ffd72 software. The

comparison of coarse and medium grids showed that the medium grid produces as expected less spurious drag than the coarse one (0.35 vs 4.66 drag counts at =13°); both values are rather low for such a multi-element configuration.

Figure 11 highlights that spurious drag is mainly created in high curvatures areas (leading edge) and in the vicinity of the slat wake (known to be responsible for near CLmax behavior), whereas the viscous drag is created in the boundary layers, in the different elements wakes and in the slat and main element coves.

Figure 10 – Flap/Side-of-body separation sensitivity to different parameters

Figure 11 – Irreversible drag creation areas; physical contributions (left), spurious (right) =50%, Config 1, =13°

=13 deg. =28 deg.

Coarse Medium Fine

Flap 25 deg. Flap 20 deg.

Config 1

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Since there is no wave drag at this Mach number for this configuration, the far-field approach only gives access to the breakdown of pressure drag (see Figure 12) between viscous pressure drag and induced drag. The latter represents more than 80% of the total drag for that AoA, as shown in Figure 12.

H. Flap deflection study The additional 5° flap deflection of Config 1 leads to extra lift, as expected (see Figure 13a)). However, the flap

efficiency decreases at high AoA. The drag increases too and is better predicted than for Config 8 (see Figure 13b)). Concerning the pitching moment, the more the flap is deflected, the higher the nose down tendency (see Figure 13c)). Figure 13d) gives a better idea of the CFD capability to predict the increments in lift and pitching moment. Although the global trends are reproduced, the relative errors are important (up to 50% on CL and 160% on Cm).

The main discrepancies with experimental data are observed on Cm prediction. This was to be expected considering the difficulty to predict flap trailing edge separation accurately for both configurations.

CLmax is over-predicted by 2% for Config 1. Some additional experimental data at high AoA for Config 8 would be necessary to evaluate the difference of stall behavior between those two configurations.

a) Lift coefficient

b) Drag coefficient

Figure 12 – Drag breakdown using a near-field (left) or far-field (right) approach (Config 1, =13°)

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c) Pitching moment coefficient

d) Lift and pitching moment increments

Figure 13 – Effect of two different flap deflection angles on aerodynamic coefficients

Since the Boeing grids do not include the brackets, we could have expected to see more lift and less drag in the calculations than in the experimental data (assuming that the wall corrections applied indeed allow to reproduce the free atmospheric conditions). Contrary to these expected trends, the computed lift and drag coefficients are in good agreement with the test data, not only in terms of gradients prediction but also in terms of absolute values. The brackets would probably have more influence in the CLmax area, which could partly explain why the gap between CFD and experiments grows at high AoA.

IV. Additional studies using the Chimera approach

A. Comparison with results obtained on coincident grids In the following figures, Str-OnetoOne-A-v1 refers to Boeing Medium grid and In-house str-overset to the

bracket-off Chimera grid created at ONERA. The results obtained on both grids are really close in terms of global aerodynamic coefficients, as can be noticed in Figure 14. A very small delay in the triggering of stall was noticed, but it is at most 1° in AoA. The agreement on drag prediction is excellent, although it is a well-known issue of the Chimera technique; friction drag varies no more than 5 drag counts between coincident and overset calculations.

a) Lift coefficient

b) Pitching moment coefficient

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c) Idealized drag coefficient d) Friction and pressure drag coefficients

Figure 14 – Comparison of aerodynamic coefficients computed with elsA on coincident and overset grids (Config 1)

Considering the little difference between both sets of grids in terms of aerodynamic results and taking into account the lesser meshing effort required by the Chimera technique for such high lift configuration, it clearly motivates the use of overset grids for future similar studies.

B. Flap support effects study Including the flap brackets helps to improve the skin pressure distribution prediction at most of span stations,

except the most outboard ones (see Figure 15d)), for which the suction gets more underestimated. Taking the flap brackets into account mainly improves the flap lower side pressure distribution prediction (see Figure 15c)). Skin pressure fields are presented in Figure 15e) and Figure 15f).

Figure 15d) shows that the flap brackets alone do not improve the skin pressure distribution prediction on the flap upper side around middle span. According to the results presented by some participants at the Chicago workshop who included the slat brackets as well, it can be inferred that those local changes in pressure are due to the slat supports.

a) Polar

b) Pitching moment coefficient

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c) Skin pressure distribution (flap area) in the flow

direction ( =28°)

d) Spanwise skin pressure distribution ( =28°)

e) Skin pressure field ( =13°)

f) Skin pressure field ( =28°)

Figure 15 – Flap support effects (Config 1) The global aerodynamic coefficient gradients are still well captured (see Figure 15a) and Figure 15b)). However,

in terms of absolute values, the trend with flap brackets is away from the experiment. The effects are, as expected, a lift loss (except at =6°) and an increase of drag (up to 225 drag counts at =13°) at a given AoA. By making the simulated geometry closer to the wind tunnel model, we could have expected to better match the experimental forces and moments data. A part of the explanation could be related to the selected test configuration, which is a half model mounted on a wind tunnel wall. That kind of mounting is not the best suited for absolute comparisons with free air CFD results.

V. Conclusion This paper focuses on the participation of the Applied Aerodynamics Department of ONERA to the first AIAA

CFD High Lift Prediction Workshop. The results presented in this article illustrate the current CFD capabilities to compute flow fields around a simplified high lift configuration.

The three test cases proposed by the organizing committee have been investigated using the ONERA-elsA solver. Coincident multiblock structured grids provided by Boeing have been used for the grid convergence and flap deflection studies, while in-house overset grids have been generated for comparison purpose but also for the flap brackets effects study. Far-Field drag analyses with the ONERA-ffd72 software have also been carried out.

The grid convergence study performed on three grid levels ranging from 22.5x106 to 170.5x106 nodes gave no real evidence of change either in skin pressure distributions or in aerodynamic coefficients. These latter are in good

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agreement with the experimental data. Another evidence of achieved grid convergence is the flap/side-of-body separation, whose topology and extension is almost independent from the grid refinement.

Predicted forces and moments are in better agreement with test data for Config 1 than for Config 8, in spite of the higher flap deflection. The computed increments of lift, drag and pitching moment between the two configurations are not predicted with satisfying accuracy, mostly because of the flap trailing edge separation issue.

The previous studies were carried out on the bracket-off configuration. An additional study of the flap support effects (the slat brackets, a lot smaller, were not modeled) showed that taking them into account improves the skin pressure distribution prediction, except for outboard stations. Their effect on the global aerodynamic coefficients is more mitigated, moving them away from the experiment.

References 14th AIAA CFD Drag Prediction Workshop, http://aaac.larc.nasa.gov/tsab/cfdlarc/aiaa-dpw/

2L. Cambier and M. Gazaix, “elsA: an Efficient Object-Oriented Solution to CFD Complexity,” AIAA paper 2002-0108, Reno, 2002.

3M. Gazaix, A. Jolles and M. Lazareff, “The elsA Object-Oriented Computational Tool for Industrial Applications,” ICASS Congress, 2002.

4J. van der Vooren and D. Destarac, “Drag/Trust Analysis of Jet-propelled Transonic Transport Aircraft; Definition of Physical Drag Components,” Aerospace Science and Technology Vol.8, No7, October 2004.

5D. Destarac, “Far-Field/Near-Field Drag Balance Applications of Drag Extraction in CFD”, VKI Lecture Series 2003-02, CFD-Based Aircraft Drag Prediction and Reduction, National Institute of Aerospace, Hampton (VA), November 3-7, 2003.

6S. Esquieu, “Reliable Drag Extraction from Numerical Solutions: Elimination of Spurious Drag,” AVT Symposium, RTO-MP-AVT-147, Paper 42, Athens, Greece, 3-6 December 2007.

7F. Gand, S. Deck, V. Brunet and P. Sagaut, “Flow Dynamics Past a Simplified Wing Body Junction,” Physics of Fluids, 2010, 22, 115111-16.