GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED 2 GARTEUR LIMITED

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3 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN Abstract Errata (known problems to be fixed before final printing): There is a remaining problem with the evaluation of integrated coefficients for the B10 case. All B10 related data should be ignored. These data will be recomputed before final printing. Results obtained within task 2 2D maximum lift prediction, task 3 2D configuration variation study and task 4 Analysis and recommendation for future work of the GARTEUR Aerodynamics Action Group AD(AG25) 2D maximum lift prediction are reported. The objective of task 2 is to perform 2D maximum lift prediction for the 59 percent span wing section of the A310 aircraft in take-off and landing configuration. These computations correspond to a selection of the 2D wind tunnel tests performed in GARTEUR Aerodynamics Action Group AD(AG08). The objective of task 3 is to demonstrate the viability of state of the art computational methods for optimization of High Lift configurations (2D configuration variation studies). From the results, it can be concluded that the overall agreement between al and CFD 2D maximum lift prediction (using state of the art CFD codes) can be qualified as good for the take-off configuration. For the landing configuration, the agreement is less good, due to the inability of the applied numerical methods and turbulence models to predict the massive separation present on the flap. Further, it can be concluded that unstructured grid techniques can bring relief with respect to the grid generation bottle neck of block structured codes, without dramatic accuracy loss compared to structured grid techniques. Finally, in order to improve on the robustness and reliability of CFD 2D maximum lift predictions, a transition prediction method capable of discriminating between different type of transition mechanisms should be implemented into the current RANS flow solvers. Task 4 summarizes the lessons learned and points out ways for continued research towards the analysis and understanding of complex high lift aerodynamics. GARTEUR LIMITED 3

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5 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN Acknowledgements The results presented in this report have been obtained within the GAR- TEUR Aerodynamics Action Group AD(AG25) entitled 2D maximum lift prediction, by the following participants: E. Saliveros BAe G. Iaccarino CIRA P. de Matteis CIRA H. Jakob DASA C. Newbold DERA S. Colman DERA R. Rudnik DLR I. Lindblad FFA L. Lorentzen FFA K.M.J. de Cock F. Moens ONERA M. Neron ONERA D. Arnal ONERA J.C. Le Balleur ONERA P. Weinerfelt Saab B. Arlinger Saab The members of AD(AG08) are acknowledged for the permission to use the wind tunnel measurements of the selected take-off and landing test cases. Dr. I. A. Lind of FFA acted as the monitoring GARTEUR responsable for the work performed in this Action Group. GARTEUR LIMITED 5

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7 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN Table of Contents 1 Introduction Experimental 2D maximum lift prediction Geometry definition D Model M3 manufacturing Matrix of flow conditions. Data reduction Experimental 2D maximum lift prediction: take-off configuration Experimental 2D maximum lift prediction: landing configuration Sources of uncertainty in 2D maximum lift s CFD 2D maximum lift prediction Geometry definition Grid generation CFD 2D maximum lift predictions for the take-off configuration CFD 2D maximum lift predictions for the landing configuration Sources of uncertainty for CFD 2D maximum lift predictions Discussion of task 2 results Comparison of CFD 2D maximum lift predictions to wind tunnel data Structured versus unstructured CFD 2D maximum lift predictions RANS versus VII 2D maximum lift predictions Turbulence modelling and CFD 2D maximum lift prediction Transition modelling and CFD 2D maximum lift prediction D configuration variation and CFD 2D maximum lift prediction Task 4: Analysis and recommendations for future work Two-dimensional versus three-dimensional high lift Experiences from computation of two-dimensional high lift flows Status of detailed viscous CFD for project analysis Recommendations for future research...41 Tables Figures Appendix A Measured quantities in the HST wind tunnel GARTEUR LIMITED 7

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9 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN Introduction The work reported here corresponds to the work definition in the proposal for the establishment of a GARTEUR Aerodynamics Action Group on 2D maximum lift prediction, see reference 1. The group is officially denoted GARTEUR Aerodynamics Action Group AD(AG25) (from here on AD(AG25), for short). Particularly, this report covers tasks 2-4 of the project, as defined in the proposal. The objective of task 2 is to perform computations corresponding to the 2D test performed in GARTEUR Aerodynamics Action Group AD(AG08) (from here on denoted AD(AG08)), see references 2-4, and to demonstrate 2D computations up to and beyond maximum lift. Both the take-off and landing configuration of the 59 percent span wing section of the A310 aircraft are considered. Take-off and landing configurations are differentiated by slat and flap settings, being more extreme for the landing configuration than for the take-off configuration. State of the art CFD methods are used to compute maximum lift for a given configuration and a specified combination of free stream Mach and Reynolds number. The current work follows earlier analysis of the data and computations, mainly using Viscous- Inviscid Interaction (VII) methods, in GARTEUR Aerodynamics Action Group AD(AG13) (from here denoted AD(AG13)), see references 7 and 8. The state-of-the-art in CFD modelling of Reynolds Averaged Navier- Stokes (RANS) and VII methods is continually improving at a rapid pace, both in terms of flow (turbulence) modelling, numerical methods and available computer resources. Although limitations in validity of currently available CFD methods for this most challenging application are well known, a systematic computation and validation (code-to-code and al) of 2D maximum lift on the A310 profile is considered as useful and necessary as part of the qualification of modern CFD for project use and for its continued improvement. The objective of task 3 is to demonstrate the viability of using state of the art computational methods for optimization of High Lift configurations. The first step, here demonstrated, is the computation of delta effects due to modification of slat and flap positions. This task is relevant for the evaluation of a high lift airfoil design between the pre-design phase (where most likely computationally cheaper methods will be used) and the wind tunnel verification phase. For this type of task, routine CFD functionality is required, hence in this task the current status of CFD methods with respect to accurate routine 2D high lift prediction capabilities is evaluated. GARTEUR LIMITED 9

10 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED Task 4 summarizes the lessons learned from AD(AG25) and gives recommendations for future research. The report is structured as follows. In chapter 2, a short description of the used AD(AG08) al results is given, since this data plays a very important role in both the AD(AG25) and in this report. In chapter 3, the computational results obtained for both the take-off and landing configuration are compared from code-to-code and with al results. In chapter 4, a general discussion is conducted about different issues evolving from the results presented in chapter 3. In chapter 5, results obtained for a single 2D configuration variation study are presented. Chapter 6 contains conclusions and a discussion of the results. 10 GARTEUR LIMITED

11 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN Experimental 2D maximum lift prediction 2.1 Geometry definition For the AD(AG08) 2D wind tunnel tests, a geometry definition of a 2D wing section has been derived from the 3D swept wing of the A310 aircraft. In figure 2 the logic for deriving the 2D wing section is given. The 59% wing station geometry is used, followed by local normalization. This is done for both take-off and landing, resulting in the appropriate slat and flap settings. The final 2D High-Lift configurations are defined in figure 3, see the local normalized sections. The notions gap and overlap are defined in figure 4. For the take-off and landing configuration, the slat and flap rotation angle, gap and overlap are given in table 1. An extended set of model coordinates can be found in reference D Model M3 manufacturing With the geometry defined, a 2D wind tunnel model M3 has been designed for HST wind tunnel measurements. In figure 5, the positions of static and unsteady pressure taps on model M3 are shown. There are 60 pressure taps on the main wing, 35 on each of the slat and flap. These numbers should be compared to the number of grid points on the different components in the computational grids. The resolution of the grids on the surface of the slat, wing and flap is usually higher. The number of pressure taps or wall grid points is important when numerical integration of pressure coefficient data is conducted to obtain force coefficients. For the HST wind tunnel tests, no wind tunnel force balance has been used, hence force coefficients are obtained from numerical surface pressure coefficient integration (for C L and C D pressure ) and wake rake surveys (for C D total ). Also boundary layer surveys and skin friction measurements are made. The positions of boundary layer surveys and skin friction measurements on model M3 are shown in figure 6. The distribution of model instrumentation on the port side and the starboard side of model M3 is shown in figures 7 and 8. Care has been taken to ensure twodimensionality of the measurements by properly activating the wind tunnel wall boundary layer blowing. 2.3 Matrix of flow conditions. Data reduction In AD(AG25) both take-off and landing configurations are considered. Prior to the actual computations, a selection of the available HST meas- GARTEUR LIMITED 11

12 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED urements has been made. The selected HST measurement flow conditions for the take-off configuration, (designated A01 through A11) are given in table 2, and for the landing configuration (designated B01 through B10) in table 3. In appendix A a full list of quantities available from the s is given. The primary data used are the pressure coefficient distributions. From the pressure coefficient distributions, integrated coefficients such as lift, pressure drag and pitching moment coefficients are derived. For case A01, A02, B01 and B02 additional measurements are available for transition locations, for velocity profiles at 7 rake stations and for one near wake station. In figure 9 the data acquisition flow chart of the wind tunnel is shown. Much effort before, during and after al testing is spent in data reduction, see figure 10 for the data reduction flow chart. When generating the aerodynamic coefficients from surface pressure integration, wind tunnel corrections are applied. Aerodynamic post processing of the obtained results is well established in the wind tunnel, see figure 11. Much effort has been made to split the contributions of the various components to the total force coefficients. 2.4 Experimental 2D maximum lift prediction: take-off configuration In figure 12 the lift coefficient versus the angle of attack is shown, for the selected test cases. Maximum lift occurs between case A06 and A07. From this curve, the compulsory and optional cases (within the Action Group) are derived: compulsory: cases A01, A02, A04, A06, A08, A10, optional: cases A03, A05, A07, A09. In figure 12 also the drag coefficient versus the angle of attack is shown. Both the pressure and the total drag are plotted. The pressure drag coefficient is the drag coefficient obtained by numerical integration of the surface pressure distribution, the total drag coefficient is the drag coefficient obtained by numerical integration of the wake total pressure loss distribution (from theoretical analysis it can be shown that for subsonic flows the total drag obtained from pressure coefficient plus skin friction coefficient integration equals the total drag obtained from far field total pressure loss integration). Clearly, in figure 12 the ally derived pressure drag is higher than the total drag, up to 260%. For this reason, the pressure drag should be ignored. Figure 12 finally shows the pitching moment coefficient versus the angle of attack. The pitching moment is negative for all cases. The absolute 12 GARTEUR LIMITED

13 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN value of the pitching moment decreases up to case A09, and increases for case A10 and A11. In figure 13 the pressure coefficient distribution around the slat is plotted for all case A01 to A11. Two of the slat pressure taps were inactive during the s. For the lower angles of attack, a separation bubble is present on the slat upper surface. For the highest angles of attack, case A10 and A11, the slat pressure coefficient distribution exhibits a wavy behaviour, which is likely due to unsteady flow (this waviness of the pressure coefficient distribution is also present for case A10 and A11 on the wing and the flap). The wing pressure coefficient distributions can be found in figure 14. The suction peak on the upper wing-slat geometric discontinuity is for most of the cases situated around a pressure coefficient of about 5, while the wing trailing edge pressure coefficient is continuously increasing. The adverse pressure gradient on the wing upper surface increases for increasing angle of attack. Wing trailing edge separation is found for the highest angles of attack. The loading of the flap decreases before the point maximum lift is reached, see figure 15. For case A09, A10 and A11, the flow on the flap upper surface exhibits wiggles. 2.5 Experimental 2D maximum lift prediction: landing configuration In figure 16 the lift coefficient versus the angle of attack is shown, for the selected landing test cases. Maximum lift occurs around case B08. From this curve, the compulsory and optional cases are derived: compulsory: cases B01, B03, B05, B07, B08, B09, B10, optional: cases B02, B04, B06. In figure 16 the drag coefficient versus the angle of attack is shown. Both the pressure and the total drag are plotted. The pressure drag coefficient is increasing in a monotonic way for increasing angles of attack, while the total drag shows an anomalous behaviour for case B01, B02 and B03. This is due to the existence of a deep separation on the flap for low angles of attack, which disappears at higher angles (see Figure 19). Figure 16 shows the pitching moment coefficient versus the angle of attack. The pitching moment is negative for all cases. The absolute value GARTEUR LIMITED 13

14 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED of the pitching moment decreases monotonically up to case B09, and then increases for case B10. In figure 17 the pressure coefficient distribution around the slat is plotted for all case B01 to B10. The loading of the slat increases continuously, up to case B09. For case B10, beyond maximum lift, the loading on the slat drops drastically. The wing pressure coefficient distributions can be found in figure 18. The suction peak on the upper wing-slat geometric discontinuity decreases steadily to a pressure coefficient of about 6, while the wing trailing edge pressure coefficient is almost constant (case B01 to B05). For case B06 to B09, the wing suction peak stays around a value of 6, before collapsing for case B10. The loading of the flap decreases before the point maximum lift is reached, see figure 19. A massive separation is present on the flap upper surface for the lower angles of attack. The separation disappears for increasing angle of attack. At the highest incidences the pressure distribution is wiggly and the massive separation returns at B Sources of uncertainty in 2D maximum lift s High-Lift devices are designed to generate high lift forces, hence the forces on the 2D model are also large. Special care has to be taken in the design of the span wise slat and flap bracket spacing, see figure 20. The deformation of the model is not measured during the test, although for maximum lift s monitoring the deformation of the model could be warranted. Due to the wind tunnel side walls, the flow in a wind tunnel is normally 3D in nature. However, the current is designed to be twodimensional and special care has been taken to ensure that this is the case. To prevent deviation from 2D flow, boundary layer blowing is applied. Ideally, the pressure distributions should be measured on different span wise locations to verify the effectiveness of the boundary layer blowing. This was however not done in the. Most of the raw data obtained from the s is corrected for wind tunnel wall interference effects in order to get rid of influence of the wind tunnel walls on the obtained measurements. This wind tunnel wall correction is a source of uncertainty because of limitations to the correctability of wind tunnel wall induced streamline curvature. 14 GARTEUR LIMITED

15 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN There is an unceartainty in the location at which a velocity profile is measured. The forces and moments of the individual elements may be difficult to separate. In the al data used here, the C L, C m, etc. data for the individual elements was integrated from the measured pressure taps, which offered a fairly limited resolution and thus introduced a large unceartainty. GARTEUR LIMITED 15

16 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED 3 CFD 2D maximum lift prediction 3.1 Geometry definition The original geometry definition used to machine the model is taken as starting point. This geometry is described in reference 6, and has finite thicknesses on trailing edges. For CFD computations, closing the trailing edges of a geometry has practical advantages, such as for instance no need to spent grid resolution to the base region, no grid topological complications, easier convergence due to the absence of (unsteady) base flow recirculations, etc. A disadvantage of the closing of trailing edges is the problem of finding a suitable closing approach, and the need for additional geometry manipulation. In order to close the geometry, two approaches can be followed: One possible approach consists of blending the trailing edges for each element. This approach would be more appropriate for the flap component because such geometric blending applied to the slat will alter the local velocities on both upper and lower surfaces at the exit of the slot and consequently affect the downstream element s suction pressure distribution. For this exercise the cove modification is performed as follows: Slat and wing cove curves/points are repositioned in such a way so that the lower trailing edge point (moving point) coincides with its upper surface counterpart (fixed point), therefore opening the slot slightly. The surface gradients of each cove element is maintained and only minor modifications to the heel region is carried out to ensure that the heel point remains as before. This modification is a little different to another possibility consisting of rotating the cove curve/points about the heel to close the trailing edge. The latter suggestion would have altered the surface gradient within the cove at the exit of the slot and therefore affected the flow behaviour at high incidences. The former approach allows continuity of surface gradients compared to the original geometry and it appears to be the more logical, even though the gap and overlap for the slat and the overlap for the flap will increase slightly. The different geometries are shown in figures 21 and 22. The closure of the trailing edges is illustrated in these figures. 3.2 Grid generation In table 4 a summary is given of the grid techniques used by the AD(AG25) partners. In this summary, the automation of the grid genera- 16 GARTEUR LIMITED

17 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN tion technique is also indicated. Note that the Viscous Inviscid Interaction (VII) method of ONERA uses an automatic structured grid generation technique. The geometry initially used for starting up the structured grid generation has open trailing edges (since grid generation started in a very early stage of task 2, it was not yet clear how the trailing edges should be closed). For the take-off case, an initial block-structured grid has been generated by Saab around the open trailing edge geometry. The grid has 13 blocks, grid points, and 468 solid wall points. This initial grid has further been modified as described below: 1. BAe closed the trailing edges of the slat and the main wing by modifying the cove regions. 2. FFA closed the flap trailing edge by modifying the trailing 50% of the pressure side of the flap. The modification (dx,dy) is a quadratic function of the arclength. It is (0,0) at ca. 50% of the flap, and grows quadratically to (dgx,dgy) at the trailing edge, where (dgx,dgy) is the trailing edge opening. In this way the surface slope remains continuous. 3. FFA modified the grid in order to fit to the closed geometry. The resulting grid is referred to as the Saab grid. 4. BAe modified the Saab grid by redistributing the points in the normal direction to ensure that y + is approximately 1 for the first grid node away from the surface. This modified grid is referenced to as the BAe grid and this is the mandatory grid. For what concerns the unstructured grids used, both DERA and use the grids generated by their own grid generators. The triangular unstructured grids used by DERA are specially adapted to the flow solver using wall functions (special requirements with respect to the distance of the first grid point away from the wall). The unstructured grids used by have 32 nodes in normal direction per boundary layer station. All the y + values are below 2 and for most of the wall grid points below 1. The unstructured grids typically have around nodes. Due to the algorithms used to generate the grids, grids with constant characteristics result. 3.3 CFD 2D maximum lift predictions for the take-off configuration Lift versus angle of attack First a note on the evaluation of CFD data for comparison to the. One source of differences between al and computational results is the difference in the number of pressure taps and wall points. Usually, more wall points are available than pressure taps. In case of large GARTEUR LIMITED 17

18 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED variations in the pressure coefficient distribution, integration of the pressure coefficient distribution can show different results. The effect is illustrated in figure 23. Using all wall points (available in the computations) results in a lift coefficient higher than the lift coefficient found when using the pressure tap locations only. Based on this observation, the pressure coefficient distributions of all the computations are interpolated to the locations of the pressure taps. After interpolation, new computational pressure coefficient distributions result which are then integrated in a uniform way (i.e. the same way for both s and all computations) to obtain comparable lift coefficients. A comparison between the various, uniformly integrated lift coefficients of the different computational results and the s can be found in figure 24. The different turbulence models used by the partners are listed in table 8. First the level of maximum lift is discussed. The VII method predicts a maximum lift level much higher than the RANS methods. This effect is discussed more in detail later, but can be attributed to the lack of confluence modelling in the VII method used. Further more, the spread between the RANS maximum lift level is smaller than the difference between the VII 2D maximum lift prediction and the al lift curve. Of the RANS methods, Saab and FFA predict a maximum lift level above the al one. The lowest maximum lift level is predicted by the ONERA. The maximum lift level predictions of other RANS codes fall in between the FFA and ONERA results. In predicting the angle at which maximum lift is reached, the RANS results can be divided into three groups. For one group of RANS results (DERA, FFA,, ONERA_NS, Saab), maximum lift occurs at angle of attack which is close to the al angle of attack. No solution beyond the maximum one is marked for FFA as the next case did not produce a converged results, C L varying at lower values. For another group of RANS results (BAe, CIRA, DA, DLR), the maximum lift angle of attack is lower than the al angle of attack. The maximum lift angle of attack found by the VII method is higher than the al angle of attack. In the same way as for the complete configuration, the lift coefficient versus angle of attack curves for the different components of the high-lift configuration separately are shown in figures 25 to 27. From these figures it can be observed that the lift on the slat continues to increase up to case A08 in the s as well as in all the computations, see figure 25. Computational results are located close to each other. 18 GARTEUR LIMITED

19 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN the lift on the wing flattens from case A02 on and starts to decrease from case A06, see figure 26. It can be observed from this figure that the different computational results can be divided in the same groups as before. the lift on the flap is continuously decreasing up to case A08, but the flap does not contribute much to the total lift coefficient, see figure 27. comparing the relative contributions, the loss in lift of the wing and flap is more or less compensated by the lift generated by the slat, up to maximum lift. This results in a rather broad maximum of the lift curve for the take-off configuration. a correlation exists between the shape of the total lift curve and the lift curve of the wing. For this reason, predicting an early decrease of the lift of the wing results in a too low maximum lift angle of attack. Several of the employed RANS models are able to model the loss of lift on the wing element, which is driving the C Lmax for this configuration (see reference 7) Drag versus angle of attack The pressure drag integrated in a uniform way is given in figure 28 for the complete configuration, and in figures 29 to 31 for the different components. In figure 28 showing the complete configuration, also the al total drag is plotted. Most computational results are found between the al pressure and total drag. For the case A01 VII results, the pressure drag becomes slightly negative due to the interpolation to the pressure tap locations and the integration process. The pressure drag coefficient delivered by the VII is of course positive. From the pressure drag plots per component, it can be observed that the pressure drag contribution of the slat is negative. Beyond maximum lift, the slat contributes most to the increase of the drag coefficient. The computation of the pressure drag coefficient of a three element airfoil from a pressure coefficient distribution is more sensitive to errors than for instance the lift coefficient. The explanation is that the slat pressure drag contribution is negative, and the absolute value of the slat pressure drag is of the same order as the sum of the wing and flap pressure drag. The total pressure drag as a consequence is a small positive number while the absolute error of the total pressure drag is the sum of the absolute errors of the slat, wing and flap pressure drag. Suppose that the absolute errors of the lift and drag computation are the same, then the relative error on the drag computation is larger than the relative error on the lift computation. Remark also that for this reason computational results should not aim for the al pressure drag (which has the same problem) but for the al total drag (measured by wake surveys). GARTEUR LIMITED 19

20 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED Pitching moment versus angle of attack The pitching moment coefficient integrated in a uniform way is given in figure 32 for the complete configuration, and in figures 33 to 35 for the different components. The pitching moment is computed around the quarter chord point of the airfoil in retracted configuration. The pitching moment of the complete configuration is negative for all angles of attack considered, but it increases continuously up to case A08. For case A10 the pitching moment of the complete configuration again starts to decrease in the s, but this effect is reproduced only by the DLR, ONERA VII and Saab results. However, similar considerations with respect to the relative error on the pitching moment coefficient hold as for the pressure drag coefficient. Now the slat pitching moment is positive, while the flap and wing pitching moments are negative and the absolute values of all three pitching moments are of the same order of magnitude, see the pitching moment plots per component, figures 33 to 35. The wing and flap pitching moment curves are rather flat, why most of the total pitching moment variation can be attributed to the slat. The pitching moment level, is governed by the flap, however Pressure and skin friction coefficient plots A reference to the different figures containing a code-to-code comparison of the pressure and skin friction coefficients is made in table 5. This table contains figure numbers referring to the code-to-code comparison of the pressure and skin friction coefficient for take-off. Note that the skin friction coefficient on the flap upper surface shows a wavy behaviour. Also in the wing leading edge region, strange skin friction behaviour is observed. In figure 42 the skin friction coefficient is compared with the properly scaled geometric curvature. From this figure, it is clear that a correlation exists between the geometry definition of the model and skin friction features. Although a common definition of transition onset locations has been specified before the computations started, it can be observed from the final skin friction results of the different partners non unique transition onset locations have been used. This is an important observation, especially for the cases close to maximum lift, because the specification of the transition onset location can largely influence the stall behaviour of a component. For case A01 ally observed transition onset locations are available. If this information is used for the slat together with a sufficiently refined mesh, a laminar separation bubble is found, see figure 43. In this figure both the pressure coefficient distribution and the skin friction on the slat are shown. If the bubble is resolved in the computations, the details of the al pressure coefficient distribution are followed. 20 GARTEUR LIMITED

21 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN Another aspect is the behaviour of the flow solvers in combination with a given grid around geometric discontinuities like the slat hook or the slat trailing edge. In figure 45 the pressure coefficient distributions around the slat hook and slat trailing edge are shown. First, the difference of the solution obtained by a Jameson type scheme (DLR) and upwind type scheme (FFA) on the same grid can be observed. This wavy behaviour of Jameson type schemes is also observed in the solutions of other partners using a Jameson type scheme. In case the geometric discontinuity is resolved in the grid by using an O-type mesh (combined with an upwind type scheme, ), the wiggles in the solution disappear completely, at the cost of spending much more grid nodes in these regions of the grid (which is still not yet feasible in 3D) Velocity profiles A reference to the different figures containing a code-to-code comparison of the obtained velocity profiles is made in table 6. This table contains figure numbers referring to the code-to-code comparison of velocity profiles for take-off. The fit between al velocity profiles and computations on the one hand, and the fit between computational profiles on the other hand is not completely satisfactory, even though the same grid has been used by a number of different partners. This observation especially holds for the resolution of the different wakes. Due to a lack of sufficient grid resolution in the wakes, both unstructured grid solutions (DERA and ) do not sufficiently resolve the wake profiles. The fit in the near wall region is somehow better than in the wake regions. This is an indication of the fact that the current RANS solvers can cope better with wall bounded flow than with wakes, and that a proper behaviour of the turbulence model in the defect layer part of the boundary layer is important for the shape of velocity profile Discussion of details in the results per partner BAe used the RANSMB code. A study of the flow for angles of attack lower than the A01 test case and angles of attack between A01 and A02 has been conducted. Transition locations are identical to the A01 case. Two grids have been used, being the FFA-Saab grid and the BAe grid. These BAe computations indicate that the stalling angle (C Lmax ) is reached at case A04, regardless of grid used. As a consequence, the cases A05 and A06 are post-stall angles (solutions over 5000 iterations have not converged, C L and C D are very unstable). GARTEUR LIMITED 21

22 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED CIRA used a low Reynolds number modification of the RNG k-ε turbulence model in the ZEN code (see reference 10) for the computations, but the results indicate that further refinement of this turbulence model is necessary. The DASA results presented in the report are obtained without the preconditioning in the FLOWer code, see reference 11. These results without preconditioning tend to show poor convergence for higher angles of attack, especially of the drag value C D. Transition points are set according to the transition points specified in the database. The lift breakdown occurs too early compared to. This is mainly due to a breakdown of the wing lift. It may be a consequence of an incorrect prediction of the confluent boundary layer between wing and slat. It can be seen that the velocity profile above the wing is not correctly predicted for higher angles of attack (see test case A02). DERA used the BAe/UMIST AIRUNS2D code for this task 2. AIRUNS2D is an unstructured, 2D only, N-S code with two turbulence models k-ε and RSM available (both models use wall functions). Various grids have been used (but not for all flow cases) 1. Grid 1 is a default with no special treatment for the take off case. 2. Grid 2 is broadly similar to Grid 1 but refined in the region of the leading and trailing edges and coves (take off case). 3. Grid 3 is the same as Grid 2 but with the first cell height reduced by 1/2 (take off case). Two types of turbulence models were used (RSM and k-ε). The results shown here are the RSM results. The differences between the results obtained with the two turbulence models are small for all cases except for case A10 where k-ε does not seem to work very well. It is observed that the results could be quite dependent on small changes to the grid. The grid used for the obtaining the results shown in this report turns out to lead to the most accurate results. The effect of transition onset location setting is found to be very large. Two sets of transition onset locations have been considered. DLR has used the same FLOWer code version as DASA, see reference 11, but with the preconditioning option switched on. As this algorithm evaluates modified eigenvalues, that are used in the time step approximation as well as in the numerical dissipation terms, it improves accuracy as well as efficiency of the numerical scheme for low speed applications. The comparison of the DA and DLR density residual convergence for case A01 in figure 44 shows, that the number of multigrid cycles required to attain a 22 GARTEUR LIMITED

23 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN given convergence level is reduced by about 40% using preconditioning. Moreover the residual oscillations are considerably alleviated. The same holds for the coefficient convergence in figure 44, especially for the drag coefficient. The improved stability behavior of the code with preconditioning enabled the computation of cases up to A10, whereas the standard algorithm used in FLOWer yielded no converged solutions beyond case A06. The additional computational effort for the preconditioning amounts to about 1.5% CPU time. The plots of the total lift and drag coefficients versus the angle of attack gives an indication of the impact of preconditioning on the numerical accuracy. The comparison between the DA and DLR results shows, that due to the reduced dissipation the lift coefficients of the main wing and of the slat increases, whereas the total drag coefficient decreases. Basically this is caused by stronger suction peaks in the pressure distribution of the slat, which tend to act as suction forces and compensate the drag forces of the other elements. The general effect of the preconditioning is to yield a lower pressure level on the upperside of all components, most pronounced in the suction peaks for higher angles of attack. As the comparison of the DA and DLR skin friction plots shows, this parameter is only slightly affected. Concerning the velocity profiles the results with preconditioning yield a better agreement with the al data for all stations and both angles of attack. The angle of attack for the maximum lift is computed lower than the al values. This is mainly caused by an underprediction of the suction peaks on the slat and the main wing. However, the maximum lift level is matched fairly well. Nevertheless, the differences between the DA and DLR results clearly proves, that, if compressible Navier-Stokes equations are solved for low freestream Mach number flows, the investigation of the numerical accuracy in terms of artificial dissipation, grid resolution etc. is of the same importance as the use of different turbulence models. Because of the complex interaction between turbulence model and main stream flowfield the present results do not allow to estimate whether an improved grid resolution alone will be sufficient to overcome the differences between the DLR results and the al data, or to which extend the well known deficiency of the k - ω model to predict free shear layers is the source of the deviations. FFA used the EURANUS code, see reference 12, with both the Baldwin Lomax and the Chien k-ε turbulence models. The results plotted in all figures were obtained using the Chien k-ε model. The lift of the complete configuration increases up to A06 with a slight decrease of slope. At A08 the solution is oscillatory, varying around lower C L values, the reason GARTEUR LIMITED 23

24 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED being slat is predicted on the slat. The Baldwin-Lomax results showed a more gentle maximum lift behaviour following the s but were considered as less interesting with the limited physics of the algebraic turbulence model. used the FANS code, see reference 13. The take-off computations are performed in several steps. First case A01 has been computed, using the original transition onset locations as specified in the database. Initial grid generation has been conducted, followed by inspection of the grid. With this experience cases A02, A04, A06, A08 and A10 are computed. For each case a new grid has been automatically generated, taking into account the user defined transition onset locations and laminar portions of the boundary layer. Convergence of the solution in terms of lift and drag is obtained for the lower angles of attack, but not for the highest angles of attack. In terms of maximum norm of the residual, for none of the flow conditions convergence to machine zero could be obtained. The specification of transition onset locations is found to be very important for both accuracy and robustness. If the transition onset location is specified to far downstream, flow solver breakdown occurs because the specified laminar flow region is not stable. ONERA_NS obtained results using the CANARI code (see reference 14) on the BAe-grid with the Saab topology. The turbulence model used is the Jones-Launder k-ε. The following strategy was used to perform the computations : first, the case A01 was computed. Then, the flow obtained for case A(n) was used to start computations for case A(n+1). For all the computations, a vortex far-field correction was applied, and computations were carried out using one grid level with low numerical dissipation coefficients (k i2 =0., k i4 =8). Multigrid routines are available in the CANARI code, but they need higher coefficients. It has to be noted that all the selected test cases were computed with a sufficient convergence rate (even for A10-A11). The transition points were set as specified in the database. However, it should be noted that the final results show that the slat and main wing upper surfaces are turbulent. This is probably due to the way the transition is implemented in the CANARI code for 2-equation turbulence models, or due to the mesh (the computed y + is around 1.2 on the upper surfaces, which is maybe still too much for a k-ε model with no wall functions). Due to that point, the ONERA-NS computations can be considered as "fully turbulent", implying an over-estimation of the viscous effects, leading to an underestimation of the lift coefficient. However, the C Lmax process seems to be well simulated by the code (figures 24 to 27) : increasing of the viscous effects on the main wing (which individual lift coefficient 24 GARTEUR LIMITED

25 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN starts to decrease around A04), due to the confluence effects, for a global C Lmax decreasing for higher angle of attack (around A07). Differences observed with can be mainly explained by the transition problem. used the VIS18B code, see reference 15. Saab used the NS2D code. Saab obtained results on the two grids, but the results shown here are computed on the compulsory grid. 3.4 CFD 2D maximum lift predictions for the landing configuration Geometry definition Both the ONERA VII code and the FANS code are capable of handling geometries with open trailing edges, for this reason the open trailing edge geometry for the landing configuration has been used. For the AIRUNS2D code the geometry of the landing configuration has been closed Grid generation Due to turn-around time limitations of the state-of-the-art structured grid generation algorithms (which are very similar for all partners using structured grid technology) no structured grid results are available for the landing case. The characteristics of the grids used by DERA, and for the landing configuration are very similar to the characteristics of the grids used for the take-off configuration Lift versus angle of attack As for take-off, the pressure coefficient distributions of the computations are interpolated to the pressure tap locations. A comparison between the various, uniformly integrated lift coefficients of the three participants can be found in figure 99. First the level of maximum lift is discussed. The VII method predicts a maximum lift level much lower than the unstructured RANS methods. This effect is discussed more in detail later, but can be attributed to the overprediction of the flap separation. The unstructured RANS 2D maximum lift prediction of DERA and is higher than the al maximum lift level, but the spread between the unstructured RANS maxi- GARTEUR LIMITED 25

26 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED mum lift level is smaller than the difference between the VII 2D maximum lift prediction and the al lift curve. For what concerns the angle at which maximum lift is reached, all methods used more or less predict the maximum lift angle of attack at the ally observed location. In the same way, the lift coefficient distributions for the different components of the high-lift configuration are shown in figures 99 to 100. From these figures it can be observed that the unstructured RANS methods overpredict the lift on all components, while the VII method shows an underprediction. the lift on the slat continues to increase up to case B09 in the s as well as in the computations, the lift on the wing increases rather regularly with angle of attack and drops for case B10. the lift on the flap is almost constant, and does not contribute much to the total lift coefficient Drag versus angle of attack The pressure drag integrated in a uniform way is given in figure 101 for the complete configuration and the slat only, and in figure 102 for the wing and flap only. In figure 101 with the complete configuration, also the al total drag is plotted. The unstructured RANS computational results are found between the al pressure and total drag. For the case VII results, the pressure drag of the complete configuration (interpolation of the pressure coefficient distribution followed by numerical integration) is negative. No original VII pressure drag coefficient has been delivered. The al pressure and total drag curves are crossing each other between case B02 and B03. The reason for this behaviour is not clear. For the higher angles of attack, the total pressure drag curves obtained with unstructured RANS methods follow the shape of the total drag curve. The same observations with respect to the relative error on the computed and measured pressure drag can be made as for the take-off configuration Pitching moment versus angle of attack The pitching moment coefficient integrated in a uniform way is given in figure 103 for the complete configuration and the slat only, and in figure 104 for the wing and flap only. The pitching moment is computed around the quarter chord point of the airfoil in retracted configuration. The pitch- 26 GARTEUR LIMITED

27 GARTEUR LIMITED GARTEUR/TP-108-2, FFA TN ing moment of the complete configuration is negative for all angles of attack considered, but it increases continuously up to case B09. For case B10 the pitching moment of the complete configuration again starts to decrease in the s. This effect is reproduced by the CFD results. The same observations with respect to the relative error on the computed and measured pitching moment coefficient can be made as for the take-off configuration Pressure and skin friction coefficient plots A reference to the different figures containing a code-to-code comparison of the obtained pressure and skin friction coefficients is made in table 7. For the lower range of angles of attack, a pressure plateau has been found on the flap upper surface in the s, as well as in the VII computations. The unstructured RANS methods do not reproduce such a pressure plateau, see figures 105 through 109. For the higher range of angles of attack, this pressure plateau on the flap upper surface has disappeared in the s, but is still present in the VII computations. For these higher range of angles of attack, the unstructured RANS methods do better reproduce the pressure coefficient distribution shape, see figures 111 through 115. On the slat and wing upper surface, the unstructured RANS methods predict a too low pressure coefficient compared to the s Discussion of details in the results per partner All DERA data have been produced using the BAe/UMIST AIRUNS2D code. Only one grid and the RSM model has been used for the landing case. The grid has a similar chord wise distribution on the surface to the take off grid2 but with slightly more points at the leading and trailing edges. The 1st cell height is the same as take off grid2 but the grid does not expand as rapidly away from the surface giving moderately better resolution of wakes. Transition positions are broadly the same as those use by. However on the slat a restriction in the AIRUNS2D code means that a point on the lower surface and one on the upper surface must be specified. For this reason transition has been put just above the leading edge, whereas has put transition just below the leading edge. For all cases the same transition onset locations are used. Very good convergence has been obtained in all cases attempted. Lift and drag converged to at least 3 significant figures. Mean and maximum density residual are reduced by at least 4 orders. The lift is overpredicted at all incidences. The maximum lift occurs at the wrong incidence and some of the subtle features of the lift curve are not reproduced. The stall behaviour after maximum lift with the sudden lift loss in Case B10 is not reproduced. Examination of the pres- GARTEUR LIMITED 27

28 GARTEUR/TP-108-2, FFA TN GARTEUR LIMITED sure coefficient distributions shows that the poor accuracy is mostly due to a failure to predict any separation on the flap. The landing case results have been computed using the 2D unstructured flow solver FANS. First case B01 is computed, followed by the other cases (restarting from the solution of the previous case) up to case B07. This approach didn t work for case B08, because the flow solution process broke down on the slat upper face. For case B08, a new grid has been generated with more resolution on the slat upper surface. For case B09 the same problem occurred, hence also for case B09 the resolution on the slat upper surface has been increased. This last grid has also been used for case B10. The lift coefficient versus angle of attack curve for the landing case is given in figure 99. For the computations, a minimum and maximum lift coefficient are indicated in case the solution is not steady. A quite large discrepancy is found between the computed and measured lift curve. The overprediction of the lift coefficient can be attributed to the underprediction of the flap separation, especially for the lower angles of attack, see for instance figure 105 for case B01. For the higher angles of attack, the flap separation gradually disappears, see figures 109 and 113, due to the increased wing wake displacement effect on the flap pressure distribution, ameliorating the pressure gradients with increasing angle of attack, see reference 9. Now the slat becomes more loaded, finally resulting in slat separation. The angle of maximum lift does correspond quite well with the al results, although the maximum lift level is somewhat larger. A Reynolds number variation has been conducted in the past at ONERA (4, 9 and 16 million) using the VII method, indicating that the flow on the flap upper surface depends strongly on the Reynolds number. This is connected with the problem of the transition prediction. At typical wind tunnel Reynolds numbers, such as the one considered in this work, the transition length on the flap upper surface, whose influence on the whole solution is very important, is long. At a higher Reynolds number, the extent of the transition region is much more limited, and the influence of the transition computation would have less importance. 3.5 Sources of uncertainty for CFD 2D maximum lift predictions Transition locations are not measured for all flow conditions and components, they are available only for case A01, A02, B01 and B02. Only the transition locations on the upper surfaces of the components are measured. For the lower surfaces, and for the other cases, a transition prediction should be made in one way or other before most of the CFD methods used 28 GARTEUR LIMITED