Aircraft Design: Aerodynamic Integration Issues

F. KafyekeManager, Advanced Aerodynamics

Bombardier AerospaceFassi.Kafyeke@notes.canadair.ca

Tel: (514) 855-52189

Introduction

The design of a modern airplane brings together manydisciplines: structures, aerodynamics, controls,systems, propulsion with complex interdependenciesand many variables. This paper illustrates the use ofCFD in the solution of a number of aircraft integrationproblems. These include the design of transonic wings,the installation of the power plant, the prediction ofwing deformation and the prediction of maximum liftcharacteristics of complete aircraft configurations.

Wing Design

In 1978, the Canadair Challenger became the first civilaircraft to fly with a supercritical wing designed withCFD methods; these methods included Jameson'sFLO22 full potential code for transonic wings, theBGK 2D transonic airfoil code, and Canadair'sMDRAG panel program for 2D high-lift airfoils. Sincethen, CFD has been used as the principal tool foraerodynamic design and development of several newBombardier jet aircraft:-The 50-passenger CRJ-200 Regional Jet (1992);-The long-range, high-speed, Global Express BusinessJet (1999);-The 70-passenger regional jet CRJ-700 (2001);-The 86-passenger regional jet CRJ-900 (2002);-The Challenger 300 Super-Midsize Business Jet(2003),

The approach to aircraft design was traditionally basedon wind tunnel testing with flight-testing being usedfor final validation. CFD emerged in the late 1960's. Itsrole in aircraft design increased steadily as speed andmemory of computers increased. Today CFD is aprincipal aerodynamic technology along with windtunnel testing and flight-testing. State-of-the-artcapabilitiy in each of these technologies is needed toachieve superior performance with reduced risk andlow cost.

Bombardier’s capability in CFD has grown since it firstbecame involved in it in 1968. Several codes weredeveloped in-house and others acquired from outside toprovide a comprehensive toolbox for the analysis and

design of complete aircraft and its components. Thesemethods, validated by wind tunnel and flight test datafrom the successive aircraft programs, are used in thedesign of new aircraft.

Advanced aerodynamic wing design methods

To design transonic wings, several methods are used.The most common is the wing shape optimizationprogram ALLOP developed in-house. Using agradient-based optimizer, it is used to match a user-supplied target pressure distribution. The control pointsof a NURBS representation of the geometry are used asdesign variables in order to optimize the pressuredistribution locally or globally. The ALLOP optimizercan call a variety of 2D and 3D high speed or lowspeed aerodynamic analysis codes. These codes returnresults that are used to calculate the current value of anobjective function to minimize. Geometric constraintsare imposed using penalty functions.

Several developments were made to the representationof wing shape in order to include typical manufacturingconstraints on the aerodynamic lines. The winggeometry is defined by a set of defining sections. Eachof these sections is represented by a 2D NURBS andthe wing surface is obtained with linear lofting betweenthe defining sections. A methodology was put in placein order to easily impose manufacturing constraints ona 3D wing geometry. The constraints, defined inplanes, are built with conics and/or lines. These conicsare themselves defined by parameters (slopes andeccentricity) that can act as design variables during theoptimization.

A second method, INDES [1], an inverse design codeoriginally developed by Tohoku University, in Japan,was linked to two transonic analysis codes. First,INDES was linked to the MGAERO 3D Euler code forcomplete aircraft of Analytical Methods Inc. TheMGAERO version used included a boundary-layercoupling introduced at Bombardier [2]. INDES wasalso linked to Bombardier’s KTRAN transonic smalldisturbance code [3] and FANSC Euler/Navier-Stokescode for complete aircraft [4]. INDES is also used tomatch a given target pressure distribution. Figure 1

shows how a typical target pressure distribution on a3D wing section is achieved by application of INDESand KTRAN. The advantage of using INDES resides inits rapid convergence.

Figure 1: Optimization with INDES and KTRAN usingtarget pressure distributions. A typical pressure distributionobtained on the wing is shown.

A third method, Epogy/AeroPointer [5], licensedfrom Engineous Corporation, is an optimizationenvironment capable of performing multi-disciplinaryoptimizations using a global parameter as an objectivefunction, such as the total aircraft drag or weight.AeroPointer was linked to Bombardier's KTRAN andFANSC transonic analysis codes. The differentcapabilities of these methods are complementary, andeach can be used effectively in the overall wing designprocess.

The main differences between ALLOP and INDES, thetwo methods for optimizing pressure distributions, istheir relative speed of execution and flexibility. Sinceit is an inverse method, INDES is significantly faster.INDES will converge or achieve its best result in some20 calls to the analysis code. In comparison, ALLOPrequires hundreds of function calls, with the length ofthe optimization depending on the number of designvariables. ALLOP optimizes the location of the controlpoints of a NURBS representation of the geometry, sothe more points are used or the greater the number ofairfoil sections, the longer the optimization will last.INDES may or may not achieve a given target pressuredistribution. If INDES does not converge on thespecified target, the best that can be done is to modifythe target pressure distribution itself. ALLOP is moreflexible because it can be restarted with a different setof design variables, and will usually continue toconverge towards the target. Typically, for a complexdesign, ALLOP must be restarted a number of times,and the complete process may last in the order of twoor three days. Unlike INDES, ALLOP will always

produce smooth airfoil sections since it uses NURBS todescribe the geometry. Another advantage of ALLOPis that it allows the user to work on a portion of a wing,or on a part of an airfoil section. For instance, the usermay optimize only the upper surface of the wing, oronly the leading edge, etc. For these reasons, INDESwill typically be used to initiate an optimizationprocess, because it does a good part of the work in ashort period of time. ALLOP is then used to refine thedesign.

The major disadvantage of using either ALLOP orINDES is the requirement to define a target pressuredistribution. This is not only a time consuming processbut it also assumes that the designer has enoughexperience to “know” what an optimal pressuredistribution is for a specific wing. In contrast, no targetpressure distribution is required when using theAeroPointer software. The latter is capable ofperforming a multi-disciplinary optimization byminimizing a global parameter, such as a combinationof the total drag and the weight of an aircraft. Thiscapability is very useful since it makes no assumptionsabout the pressure distribution, and it effectivelyautomates the design process.

Automation in the design process is important not onlyfrom the point of view of efficiency, but also because itmakes multi-disciplinary optimization possible, sincethe latter can only be done through the minimization ofglobal parameters. AeroPointer achieves this capabilitythrough the use of a hybrid optimizer that combines thecapabilities of genetic, gradient, and simplex methods.AeroPointer also allows the user to define anygeometric parameter as a design variable or aconstraint, and can perform weighted multi-pointoptimizations. Naturally, the quality of the final designwill depend on the fidelity of the analysis code and onthe topology of the design space. Typically, a carefulselection of design variables and constraints is requiredto ensure a successful optimization, and themethodology developed for one application may notnecessarily be optimal for another. In some cases,AeroPointer will produce a good design but one thatcan clearly be improved in some areas. In such a case,an AeroPointer optimization would be followed by thefurther optimization of the pressure distributions usingeither INDES or ALLOP.

The current best approach therefore is to initiate thedesign process with AeroPointer to define the generalcharacteristics of the optimal design in an MDO sense(Figure 2). This process would typically be initiatedwith a low fidelity analysis code and completed with ahigher fidelity code whenever practical. Once thegeneral characteristics of the configuration have been

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Figure 2: (a) Initial business jet configuration; KTRANsolution; M=0.8 CL=0.5. (b) Configuration optimized withAeroPointer/KTRAN; M=0.8 CL=0.5

Figure 3: Pressure distribution achieved with an MDO senseoptimization compared to a final design including a locallyrefined pressure distribution.

CFD flow analysis and drag prediction

The accurate prediction of drag and its differentcomponents is essential in wing design. BombardierCFD development efforts have concentrated on the FullAircraft Navier-Stokes Code FANSC [4]. The programuses multi-block structured grids, with an unstructuredblock topology, i.e. it allows any number of blocks tomerge at the same location. It uses a cell-centered finitevolume approach with a choice of space-discretizationschemes and an explicit Runge-Kutta time-marchingmethod. The code can be run in Euler mode, in Euler

mode with boundary-layer coupling and in Navier-Stokes mode. The Navier-Stokes code uses the Spalart-Allmaras and k-ω turbulence models [4].

To be useful in a realistic design environment, theFANSC code was made robust for solutions oncomplex aircraft geometry. Boundary conditionsinclude no-slip and slip walls, transpiration wall forboundary-layer coupling, symmetry and degeneratelines and points, Riemann and engine inlet/outletboundary. The code allows also the specification ofmultiple boundary conditions on each block face. Itsrun time efficiency was considerably improved byadding coarse grain parallelization on blocks (3.6 outof 4 CPUs) and vectorization (94% efficient). Severalpre-processing features were implemented to detecterrors in the topology or grid file. Specific solver/gridquality criteria were constructed to ensure successfulflow analyses.

The large CPU time of Navier-Stokes computationsstill precludes their inclusion in routine design andoptimization loops, unless and adjoint formulation ofthe derivatives is used. Euler/boundary-layercomputations are used instead. A boundary-layer codewas developed and coupled with FANSC first throughthe use of a direct Viscous/Inviscid Interaction (VII)scheme [6]. The coupling uses a transpiration velocityapproach, with no need to regenerate a new mesh atevery VII cycle. Since a direct VII procedure failswhen separated flow is encountered, as often foundduring design iterations, an inverse boundary-layercode was also coupled with FANSC using a quasi-simultaneous VII scheme. The viscous flow is solvedwith the CIBL3D inverse code, developed by Cebeci etal. at California State University, Long Beach [7].With this code, separated boundary layers can becomputed with accuracy comparing favorably withmore time-consuming Navier-Stokes computations formany cases of interest. This is illustrated in Figure 4,showing a pressure distribution computed at mid-spanof a Challenger wing-body configuration.

To be used effectively in aerodynamic design loops,CFD codes must produce accurate, reliable andrepeatable drag estimates. Drag modules wereconstructed as a post-processing step to theEuler/Boundary-layer solutions. They include a semi-empirical module for fuselage and nacelle drag, aMulthopp algorithm for induced drag, Lock’s methodfor computation of wave drag based on shock strengthand a Squire-Young module for the computation ofwing and tailplane viscous drag. Far-field methods forthe induced drag and the computation of wave dragfrom the integration of entropy variation across shockwaves were also investigated.

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Figure 4: Euler/Boundary layer and Navier-Stokescomputations of flow on the Challenger wing/bodyconfiguration Mach 0.82, Alpha = 1.5 degrees, Rec = 6Million, station at 40.5% of the wing semi-span.

More recently, investigations were made in theprediction of drag from direct integration of pressuresand skin friction obtained with a high-accuracy Navier-Stokes solution. The difficulties of predicting dragthrough Navier-Stokes computations were illustrated attwo AIAA Drag Prediction Workshops held in June2001 in Anaheim, California [8] and in June 2003 inOrlando, Florida [9]. In the first workshop, FANSCwas used to predict the drag polar of a DLR-F4 wing-body for which experimental results had been collectedin NLR, ONERA and DRA wind tunnels. Drag wasobtained from integration of pressure and skin frictioncoefficients, as specified for the workshop. Initialpredictions made by FANSC with the grid supplied bythe workshop organizers showed drag levels muchhigher than experimental values (DPW grid results, inFigure 5). A new mesh of the DLR-F4 generated usingBombardier’s MBGRID program [10] was prepared.The mesh had good orthogonality on the solid surfaces,10-6 chord wall spacing, 3.8 Million mesh points, anopen wing tip and a blunt trailing edge. Calculationswith the same program on this mesh showed excellentcorrelation with the experimental values (BBD gridresults, in Figure 5). The main difference between thetwo grids was in the orthogonality near solid surfaces.FANSC showed excellent convergence characteristics(density and turbulent viscosity) on the Bombardiergenerated grid and on the workshop supplied grid,despite its excessive skewness. Integrated lift andpitching moment predictions on the workshop gridwere accurate. Mesh skewness introduceddiscretization errors on the skin-friction evaluationswhereas pressure drag was correctly predicted on bothgrids. This illustrates the great care that must be

exercised if drag from Navier-Stokes computations isused as the function to be minimized in a wing designprocess. One must ensure that mesh modifications donot introduce variations not due to the wing geometrychanges.

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Figure 5: FANSC prediction of drag polar for the DLR-F4configuration, Mach 0.75, Reynolds number 3 Million.

Figure 5 shows that an excellent drag polar was alsoobtained on the DLR-F4 configuration with FANSCrunning in Euler/boundary-layer mode with the post-processing drag formulas. The grid required for thiscalculation was much simpler, with 1.3 million gridpoints. A solution for one angle of incidence isobtained in 0.45 hours on 8 CPUs of a Cray SV1computer instead of the 6 hours required by the Navier-Stokes calculation. The Euler solution required 600Mbytes memory instead of the 2.2 Gbytes required bythe Navier-Stokes analysis. There are thereforeadvantages in using Euler/boundary-layer methods inlarge parts of the wing design process.

In the second workshop, in 2003, results on the DLR-F6 wing-body and wing-body engine configurationswere obtained on several meshes. Comparison of theflow solutions computed on all the above meshesshowed that good results were obtained with theMBGRID mesh. The residuals for calculations on awing-body converge to machine accuracy with a linearconvergence rate (Figure 6). Accurate flow solutionswere obtained on the DLR-F6 wing-body-underwingengine, as shown in Figure 7. However, the predicteddrag, shown in Figure 8 for various grids, still requiresimprovements, particularly for the wing/body/enginecase.

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Figure 7: FANSC results on the DLR-F6 wing-body-engineconfiguration (Mach 0.75, Re=6.0 Million).

Figure 8: FANSC results on the DLR-F6 wing-body-engineconfiguration (Mach 0.75, Re=6.0 Million).

Additional efforts in drag prediction are focused onderiving a rigorous mathematical model for computing

the induced, viscous and wave drag from Navier-Stokes solutions. The method uses cell-by-cell entropyintegration to compute the drag generated within eachcomputational cell. Using conservative laws, theirreversible viscous and wave drag flow phenomenacan be computed separately. The reversible dragphenomenon, induced drag, is computed using cross-flow analyses. One major difficulty is theidentification of the “spurious” drag produced by theartificial dissipation present in the flow solver tostabilize the iterative flow procedure.

Despite considerable progress made to date, the use ofNavier-Stokes methods in aircraft design integration isstill a challenge. The best approach seems to be the useof a full suite of low and high fidelity codes, startingfor instance with low fidelity codes and finishing withthe more sophisticated methods. The implementation of3D Navier-Stokes codes in design requirescomputation of the sensitivities through the solution ofadjoint equations. This approach works very well forthe design of two-dimensional single and multi-element airfoils and for simple wing/bodyconfigurations. However, for complete aircraftconfigurations, it still requires and a significantupgrade of the available computing hardware to befully effective.

Aerodynamic analysis of transonic flexible wings

One important aspect of wing design is fluid-structureinteraction. The objectives are the prediction andminimization of wing weight, the prediction of thewing structural deformation under loads (Figure 9) andthe influence of this deformation on the aerodynamicload distribution. The prediction of the bending andtwisting of wings was achieved by coupling thetransonic CFD code KTRAN [3] with a thin-walledstructural analysis program TWSAP [11]. The linearstructural capabilities of the NASTRAN structuralanalysis software were utilized to predict the bendingand twisting of simplified finite element models (stickmodels) of actual wings. Deformations predicted usingthese stick models were in very good agreement withthe results of full Finite Element Models (FEM) [11].Results obtained for the static equilibrium(convergence) state of the Challenger and GlobalExpress wings in 1g-flight were found to be in verygood agreement with experimental data. In a followingstep, the methodology was extended to predict theaero-elastic deformation of wings at the conceptualdesign stage. The program generates conceptuallayouts of wing structural components and creates abeam finite element model of the wing structure(Figure 10). To establish the accuracy of the stick

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model designed by TWSAP, the predicted wingbending and twisting were compared with resultsobtained with the full finite element model of the realwing structure, without winglets. It can be seen inFigure 11 that the accuracy obtained is sufficient forpreliminary design purposes.

Figure 9: Static aeroelastic deformation of a transonic wingcomputed by the KTRAN/TWSAP/NASTRAN package atMach 0.80 and CL=0.5

Figure 10: Challenger wing structure and examples ofconceptual structures generated by the TWSAP program.

Figure 11: Comparison of wing bending predicted by theconceptual stick model and the full FEM of the Challengerwing without winglets.Power-plant Integration

The integration of aft-fuselage-mounted enginenacelles requires a major effort to reshape the fuselagein the nacelle/ pylon area and optimize the pylondesign. This process can be illustrated by thedevelopment of the Global Express. The BombardierGlobal Express is a business jet specifically designedto provide comfortable ultra-long range at a higherspeed than any other aircraft in its class. With aMaximum Take-Off Weight of 93,500 lbs, the GlobalExpress can fly eight passengers and fourcrewmembers over a distance of six thousand nauticalmiles, at Mach 0.85. The aircraft was also designed toprovide excellent runway performance. The main aero-dynamic features of the aircraft are: a high aspect ratio,35-degree swept wing with winglets, using thirdgeneration supercritical airfoils, a T-tail configurationand two fuel efficient turbofan engines mounted on theaft fuselage to keep the wing free from adversenacelle/engine interference.

Figure 12: Cartesian and cylindrical grids used byBombardier’s KTRAN transonic small disturbance code forfull aircraft configurations.

The objective in this case was the elimination ofundesirable shock waves that appeared on the lowersurface of the pylon and the nacelle at cruise conditionsabove Mach 0.8. The shaping of the fuselage was firstcarried out with the aid of the fast transonic smalldisturbance code KTRAN [3]. This code uses aninternally generated Cartesian and cylindrical meshsystem that includes an overall coarse grid and finegrids for the wing, the body/nacelle and for thewinglets, as shown in Figure 12. The pylons are notincluded in the calculations. Figure 13 shows isobarsobtained with KTRAN on fuselage and nacelle at Mach0.85 at three stages of the development of the GlobalExpress.

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The fuselage shape obtained from these calculationswas used as input to the FANSC Euler/Navier-Stokescode to check the flow situation with the addition ofthe pylon. The pylon and the nacelle position, in termsof incidence and toe-out angle were optimized with theaid of FANSC. Figure 14 shows the Euler body-fittedstructured mesh generated for the Global Expresscomplete configuration. Figure 15 shows the isobarsobtained on fuselage, nacelle and pylon with theFANSC Euler/Boundary Layer code, at Mach 0.85cruise conditions, at three stages of the aircraft design.

Figure 14: Multi-block structured grid generated for theGlobal Express complete configuration using Bombardier’sMBGRID program.

Figure 16 shows comparisons of FANSC calculationswith wind tunnel test results obtained at the ARAtransonic wind tunnel (Bedford, U.K.).It can be seenthat FANSC predicts well the shock on the lower sideof the original pylon, and the shockless pressuredistribution of the optimized pylon at Mach 0.85 cruiseconditions.

Figure 15: FANSC study of Global Express aft fuselagepressure distributions at three stages of the design. Mach 0.85cruise conditions.

Figure 16: Channel pressure distributions at M=0.85;Comparison of FANSC computations with test data.

High-Lift Systems Design

The development process of an aircraft high-liftconfiguration includes CFD design and analysis, windtunnel tests and flight tests, as illustrated by thedevelopment of the flaps and slats of Bombardier’sCRJ-700 70-passenger Regional Jet (Figure 17)[12].

Figure 17: Bombardier CRJ-700 with flaps and slatsdeployed in landing configuration.

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The CRJ-700 was designed to carry seventy passengersand three crewmembers over a range of 1685 NauticalMiles (1985 NM for the extended range version) atMach number 0.78. Maximum operating Mach numberis 0.83 and maximum cruising altitude 41,000 feet. Theaircraft, designed with a Maximum Take-Off Weight(MTOW) of 73,000 lbs (75,250 lbs for the extendedrange version), is powered by two fuselage mountedGeneral Electric CF-34-8C1 turbofans, eachdeveloping 12,670 lbs thrust. The take-off distance atMTOW is 5,130 ft and the landing distance, at theMaximum Landing Weight of 67,000 lbs is 4850 ft.Figure 18 shows the planform definition of the CRJ-700 high-lift system. The trailing edge flaps aredouble-slotted hinged flaps. The flap deflection anglesare 10 and 20 degrees for take-off, 30 degrees forapproach and 45 degrees for landing. The leading edgeslats cover the full span except for an inboard segmentof the wing, which was left unprotected to improve thestall characteristics. The slats are tapered, covering15% of the wing chord at the break and 17% chord atthe wing tip. The slat deflections are 20 degrees for 0degrees and 10 degrees flap settings and 25 degrees forall other flap settings.

Figure 18: CRJ-700 flaps and slats planform definition

The development of the high-lift systems wasconducted using Computational Fluid Dynamics (CFD)methods. Initial work focused on the design of multi-element airfoils at various span wise stations on thewing. Design variables included the shapes of the slatand flap and the optimum gap, overlap and deflectionangle of each one. The CEBECI or CSU code, aviscous panel method with strong boundary layercoupling developed by T. Cebeci and his team at theUniversity of California in Long Beach [7] was usedfor rapid evaluation of alternative designs. The MSESviscous Euler code of Drela [13] and the NSU2D 2Dunstructured grid Navier-Stokes code developed by D.Mavriplis [14] were used for the detailed design and

final verification of flaps and slats. The accuracy ofthree codes was verified using wind tunnel test dataobtained on a Bombardier experimental high-lift model[15]. Figure 19 shows that all three codes predict fairlywell the characteristics of the single-element airfoil.The figure gives also results obtained with Tornado, amulti-block structured grid Navier-Stokes codedeveloped by University of Toronto [16]

Figure 19: Lift and drag predictions of various CFD codeson a clean airfoil configuration; comparison with wind tunneldata on a Bombardier experimental airfoil.

The main aerodynamic issues of high-lift systemdevelopment are: the prediction of the CLmax and stallcharacteristics of complete aircraft configurations withthin swept wings; the prediction of roughness effect onthe CLmax of these wings and the prediction of lift-to-drag ratio of take-off configurations.

One method frequently used to estimate the 3Dmaximum lift of a full aircraft involves a combinationof a panel method coupled with 2D empiricalcorrelations, as reported in the work of Valarezo andChin [17]. The original work was used to predict themaximum lift of arbitrary 3D configurations withoutroughness, based on the Pressure Difference Ruleconcept. This is an empirically derived correlation thatrelates the maximum pressure differential as predictedby simple linear panel methods to the experimentallymeasured maximum lift condition. In other words, fora given Reynolds and Mach number combination,empirical curves have been determined which give themaximum “inviscid” adverse pressure gradient acorresponding 2D boundary layer can sustain beforeseparation occurs. This empirical limit is then used todetermine the maximum lift of a 3D configurationbased on the sectional pressure distributions.

Figure 20 shows the good correlation obtained betweenpredictions and wind tunnel test results for a CRJ-200

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cruise configuration. In [18], a simple method isintroduced as an extension of the Pressure DifferenceRule that allows the estimation of the maximum lift ofan aircraft with wing leading edge contamination.Aircraft certification regulations stipulate that anaircraft handling characteristics and performance mustbe determined for flight in icing conditions and inroughness conditions. The roughness can be caused byice, frost, de-icing and anti-icing fluids used prior totake-off, insect contamination, paint and surfaceirregularities and leading edge damage such as thatproduced by a hail-storm. Roughness on the wingleading edge affects the stall characteristics of anaircraft and its performance.

The method is based on a combination of the PressureDifference Rule, using a three-dimensional panelmethod, with results of a two-dimensional interactiveviscous-inviscid CFD procedure developed by Cebeci[7]. The code is able to predict aerodynamicperformance of single and multi element airfoils,including stall, with and without surface roughness,with sweep effects (Quasi 3-D), for steady flows. Thecode uses a Hess and Smith panel method to calculatethe inviscid flow field with a simple Karman-Tsiencompressibility correction formula. A two-dimensionalcompressible boundary layer code operating in aninverse mode, is coupled to the panel method. Michel’sformula is used for transition prediction and animproved Cebeci- Smith model is used for turbulencemodeling with roughness effects. Roughness effects areintroduced using the Cebeci-Chang model [19]. Theequivalent sand grain roughness is the inputcharacterization parameter for the code.

Figure 20: Predicted maximum lift with and withoutroughness; comparison with experimental dataThe method is illustrated in Figure 21, using a model ofthe M100 ONERA3 wing/body test article. In this

application, the VSAERO panel method of AnalyticalMethods Inc. is used. An initial VSAERO analysis isfirst conducted to determine the critical spanwiselocation where the maximum pressure differenceoccurs. Based on the local chord Reynolds number atthat critical section, a 2D analysis is conducted todetermine the incremental effects due to roughnesswith on maximum lift. The figure shows the 2D liftcurves calculated with and without contamination. Thisincrement is applied to the original limit ?Cp curveand compared with the original spanwise distributionsof ?Cp to determine the new maximum lift point withcontamination. Figure 21 shows the limit ?Cp curveswith and without roughness as well as the spanwisedistributions of ?Cp as calculated using VSAERO forseveral angles of attack. Finally, Figure 21 shows thepredicted maximum lift for the configuration with andwithout roughness. The methodology was validatedusing the results of the wind tunnel tests carried out ona 1/3 scale model of a regional jet. Tests wereconducted at Mach 0.15 and mean chord Reynoldsnumber of 2.72 million, for various levels of wingcontamination. Figure 20 shows the comparison ofpredicted and experimentally measured maximum liftcoefficients with and without contamination for thecruise configuration. The relative loss in lift due tocontamination compares well with experiment,although the absolute levels are slightly over-predictedin this case.

Figure 21: Application of the extended pressure differencerule to the prediction of maximum lift with roughness effects.

An application of a Navier-Stokes method to theinvestigation of an aircraft maximum lift is reported in[20]. The NSU3D unstructured Navier-Stokes solver isused for the study [21]. It uses an edge-based, vertex-centred finite-volume scheme for space discretisation

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and a multi-stage Runge-Kutta technique for timeintegration with point or line pre-conditioning. Anagglomeration multigrid algorithm is implemented forconvergence acceleration. Two turbulence models areimplemented: the Spalart-Allmaras model and theWilcox k-ω model. Both can be used with or withoutwall functions.

The Challenger wing/body/nacelle configuration wasselected to investigate the ability of NSU3D to predictflows at high angles of attack up to and beyond stall.The geometry modelled represents the wind tunnelmodel including flap fairings and flow-throughnacelles. The flow conditions of the wind tunnel dataused for comparison are a Mach number of 0.25 and aReynolds number of 2.2×106 , based on the wing meanaerodynamic chord. The stall pattern on this model istypical of transonic jets with no slats or leading edgeflaps. A leading edge flow separation, due to thebursting of a laminar short bubble, causes a sudden lossof lift at stall.

Figure 22: Post-stall isobars and skin-friction lines on aBusiness Jet clean-wing model at Mach 0.25 and a=14.21o ;NSU3D Navier-Stokes solution with a k-? turbulence model.

The relative performance of the Spalart-Allmaras andk-ω turbulence models in predicting the lift variationwith incidence was evaluated on this mesh. Post-stallisobars and skin-friction lines computed at a=14.21o

using the k-ω turbulence model are shown on Figure22. The predicted lift variation with incidence for thetwo turbulence models is compared with theexperimental data in Figure 23. These results wereobtained with the assumption of fully turbulent flow.At incidences up to 10°, both turbulence models predictlift fairly well. At higher incidences, however, thepredicted lift is lower than the experimental data beforestall, with the one-equation Spalart-Allmaras modelresults being worse than those obtained with the two-equation k-ω model. Both models underpredict the pre-

stall lift coefficient, due to an excessive amount ofpredicted separated flow on the outboard wing. Noneof the numerical results predicts the sudden drop of liftafter stall, but the Spalart-Allmaras predictions show akink in the lift variation shortly after the experimentalstall incidence.

Figure 23: Comparison of predicted lift curves withexperimental data

Modelling laminar flow at the leading edge of the wingimproves marginally the results but it is fair toconclude that the present models need improvementsbefore they can predict correctly the maximum liftbehavior of three-dimensional configurations.

Figure 24: VSAERO solution for a CRJ-700 early landingconfiguration, flap setting: 45 degrees, slat setting: 28degrees Mach 0.2, Alpha=10 degrees

The analysis and design of the CRJ-700 high-liftsystem was therefore carried out using the extended

α (°)

CL

0 5 10 15 20

Wind tunnel datak-ω, fully-turb.S-A, fully-turb.

CL-601 WBNB82 W50 F5 N51 P54

M = 0.25 ReMAC = 2.2×106

Mesh 2.5.2

pressure difference rule. Figure 24 shows a VSAEROmodel of the landing configuration of the CRJ-700.The predicted CLmax of a clean configuration comparedvery well with high Reynolds number wind tunnel data,as shown in Figure 25.

Figure 25: Comparison of wind tunnel data with theoreticalprediction of CLmax for the CRJ-700 clean configuration atwind tunnel conditions: Mach 0.20, chord Reynolds number6.5 million.

Figure 26: Comparison of trimmed lift curves from theIAR 5-ft wind tunnel test with flight test data.

The high-lift system design was ultimately validatedduring an extensive flight test program. The aircraft isfitted with a stall protection system including a stickshaker for stall warning and a stick pusher for stallrecovery. The stick pusher activates ahead of naturalstall for the clean configuration and post-natural stallfor all configurations with the slats deployed. Figure 26shows lift curves from a high-Reynolds number windtunnel test [12], trimmed for the most forward centre of

gravity, compared to equivalent data from performanceflight tests. This figure shows good correlation for allflap angles.

Conclusions

The aerodynamicist designing and developing thedetailed geometry of a high performance aircraft isfaced with several issues, some of which are: how toachieve the optimum cruise configuration for themission required, how to account for the flexibility ofthe wing, how to install large turbofans with minimuminterference, how to define optimum flaps and slats forfield performance? Aerodynamic computations withCFD methods provide answers to many of thesequestions. The judicious use of CFD methods, frompanel methods to Navier-Stokes solver, allow theaerodynamicist to arrive at feasible configurations withminimum cost and risk.

The technology for simulating wing flows hasprogressed to a point where routine Navier-Stokesanalyses of complete aircraft configurations arepossible. However, progress is still required to makethe prediction of drag from these methods morereliable. The full advantage of Navier-Stokes methodswill be reached when they will be used routinely for3D full aircraft design. This requires the calculation ofdesign sensitivities using the solution of adjointequations and even more powerful computers.

Acknowledgement

The author would like to acknowledge the contributionof the staff of Bombardier Advanced Aerodynamics,notably Pat Piperni, Eric Laurendeau, Marc Langlois,Josée Boudreau, David Leblond and Cedric Kho.

α (deg)

Lift

Coef

fic

ient

(CL)

Predicted CLmax

α (deg)

Lift

Coeffici

ent

(CL)

- - - - - - 5 x 5 Wind TunnelFlight Test

slat

25o

flap

45o

slat

20o

flap

0o

slat

20o

flap

10o

slat

25o

flap

20o

References

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