Numerical simulations of 3D aircraft configurations are performed in order to understand the effects of turbulence models on the prediction of aircraft's aerodynamic characteristics. An inhouse CFD code that solves 3D RANS equations and twoequation turbulence model equations are used. The code applies Roe’s approximated Riemann solver and an AFADI scheme. Van Leer’s MUSCL extrapolation with van Albada’s limiter is also adopted. Various versions of Menter’s
kω
SST turbulence models as well as Coakley’s
qω
model are incorporated into the CFD code. Menter’s
kω
SST models include the standard model, the 2003 model, the model incorporating the vorticity source term, and the model containing controlled decay. Turbulent flows over a wing are simulated in order to validate the turbulence models contained in the CFD code. The results from these simulations are then compared with computational results from the 3
^{rd}
AIAA CFD Drag Prediction Workshop. Numerical simulations of the DLRF6 wingbody and wingbodynacellepylon configurations are conducted and compared with computational results of the 2
^{nd}
AIAA CFD Drag Prediction Workshop. Aerodynamic characteristics as well as flow features are scrutinized with respect to the turbulence models. The results obtained from each simulation incorporating Menter’s
kω
SST turbulence model variations are compared with one another.
Nomenclature

bWing Span

CLLift coefficient

CD,CDf,CDpTotal drag, skin friction drag, pressure drag

Cω1,Cω2,Cω3,Cq1Model constants ofqωturbulence model

kTurbulent kinetic energy

pPressure

qTurbulent velocity scale

ywNearest distance to the wall

Spanwise distance measured from fuselage centerline

α, β, β*Model constants ofkωSST turbulence model

μmMolecular viscosity

μtTurbulent viscosity

σS1, σS2Model constants of the turbulence models

ωTurbulent dissipation rate

Ω Absolute value of vorticity
1. Introduction
Computational Fluid Dynamics (CFD) is a major research tool for aircraft design and analysis. However, predicting aerodynamic characteristics is difficult because of aircraft's complex flow features and configurations. Turbulent flow comprises the majority of the flows around an aircraft; it is often accompanied by large separations, wall shear layers, and shocks. Resolving the entire range of turbulent length scales is necessary for accurately analyzing the flow. However, the high computational expenses connected to Direct Numerical Simulation (DNS) forces us to use some sort of turbulence modeling in the simulation of complex flows around aircraft. The Reynolds Averaged NavierStokes (RANS) equations are more effective than Large Eddy Simulation (LES) for turbulence modeling. However, the choice of turbulence models strongly influences the RANS solutions. Thus, selecting an appropriate turbulence model requires special care and attention.
The aerodynamic characteristics of an aircraft were researched at the AIAA CFD Drag Prediction Workshops (DPW). At the 2
^{nd}
AIAA CFD DPW (DPW2)
[1]
, the DLRF6 wingbody (WB) model and wingbodynacellepylon (WBNP) model were selected for drag prediction. Experimental data and results from the participants in DPW 2 are presented on the website
[1]
as well as at the 42
^{nd}
AIAA Reno conference. At the 3
^{rd}
AIAA CFD DPW (DPW3)
[2]
, the DLRF6 wingbody with FX2B fairing transport model and wingonly models (DPWW1 and W2) were selected to predict aircraft forces and moments. The FX2B fairing reduced separation at the wingbody juncture. DPWW1 and W2 are transonic wing models suitable for validating the code. Experimental data and numerical results are summarized on the website
[2]
.
The purpose of this paper is to evaluate the aerodynamic characteristics of 3D aircraft configurations according to the various turbulence models. This is undertaken using an inhouse CFD code, which is validated through comparison between the numerical results and other computational results for the DPWW1 model. Numerical simulations of the DLRF6 WB model and the WBNP model are then performed using various turbulence models including the
qω
model, the
kω
SST model, and the variations of the
kω
SST model. The aerodynamic characteristics and flow features resulting using these models are compared to experimental data and other numerical results.
In this paper, the governing equations and numerical schemes for the mean flow are first presented. Next, various turbulence models are briefly discussed. The simulation results of the DPWW1 model are presented to show the validity of the code. The aerodynamic characteristics of the DLRF6 WB and WBNP models are presented and compared with other research results.
2. Numerical methods
 2.1 Governing Equations and Numerical schemes
Threedimensional RANS equations and the twoequation turbulence model equations are chosen as the governing equations for the simulation of the turbulent flow around an aircraft. The equations are
where
W
denotes the conservative flow variable vector,
E, F
and
G
are the inviscid flux vectors;
E_{v}, F_{v}
and
G_{v}
are the viscous flux vectors in each spatial direction.
S
is the source term vector from the turbulence model equations. The conservative flow variable vector and flux vectors are defined as
where
ρ, u, v, w, e, s_{1}
and
s
_{2}
are the density, velocity components in each directions, specific total energy, and turbulence variables, respectively.
τ_{ij}
and Ω
_{i}
are the total stress tensor and the total energy flux vector, respectively. The equation of state for an ideal gas is used.
The governing equations are solved using a cell centered Finite Volume Method (FVM). Roe’s approximated Riemann solver
[3]
is used for computing the inviscid flux; the central difference method is employed for the viscous flux. Van Leer’s MUSCL extrapolation
[4]
with a limiter is used to obtain secondorder accuracy while maintaining the Total Variation Diminishing (TVD) property. An Approximate FactorizationAlternative Direction Implicit (AFADI) scheme
[5]
is used for the steadystate solution.
 2.2 Turbulence models
Coakley’s
qω
model
[6]
and the various forms of Menter’s
kω
SST model
[7]
are used to compute the turbulence quantities.
 2.2.1 Coakley’s qω model
The turbulent velocity scale,
q
and the specific dissipation rate,
ω
are used as the turbulence variables for Coakley’s
qω
model in order to estimate the eddy viscosity. The model used in this study is the baseline model with an added compressibility correction. The eddy viscosity is computed through the PrandtlKolmogorov relation
where
Cμ
=0.09. The damping function is defined by
where
y
is the normal distance from the nearest solid wall. The source term vector is written as:
 2.2.2 Menter’s kω SST Model (SST) and Its Various Versions
Menter’s
kω
SST Model is developed to predict accurately aeronautical flow exhibiting strong adverse pressure gradients and separation. The eddy viscosity of the SST model is
The source term vector is defined as
The other model constants are determined with blending function
F
_{1}
.
 2.2.3 SST Model with the Vorticity Source Term (SSTV)[8]
The vorticity magnitude is more easily computed in comparison to the exact source term. The vorticity magnitude is generally very close to the exact source term in simple boundary layer flows. In this model, the term
P
is redefined as
 2.2.4 SST Model from 2003 (SST2003)[9]
The SST2003 model redefines the eddy viscosity term. Eddy viscosity is computed using the strain invariant rather than the vorticity magnitude.
where
While the production limiter is used for the
k
equation in standard model, the production limiter is applied to both
k
and
ω
equations. Other minor changes can be found in Menter et al.’s study
[9]
.
 2.2.5 SST Model with Controlled Decay (SSTsust)[10]
For external aeronautical flows, the SST model with controlled decay (SSTsust) prevents the nonphysical decay of the turbulence variables in the freestream. Sustaining terms are added to the source term in the turbulence equations; the changed source term vector is therefore redefined as follows:
3. Computational results
 3.1 DPWW1 Configuration
Numerical simulations of the DPWW1 configuration are conducted to validate the CFD code. The results are then compared with computational results by Tinoco
[2]
. The Mach number of the flow is 0.76 and the Reynolds number is 5.0× 10
^{6}
based on a Mean Aerodynamic Chord (MAC) length of 197.556 mm. The angle of attack (AOA) is 0.5 degree. The grid used in Tinoco’s research is employed in these simulations. The
kω
SST model and the
qω
model are used for turbulent viscosity. The geometry of the wing and the two span locations
used for the comparisons of pressure distributions are presented in
Fig. 1
. Pressure coefficient distributions around the upper surface are also shown. Comparisons between the pressure distributions
Comparison of pressure distributions at two span locations (DPWW1, AOA : 0.5°)
in the two positions are shown in
Fig. 2
; the pressure distributions correlate well with Tinoco’s results except close to the shock locations, where there are slight differences in pressure. However, determining which results are more accurate is difficult without experimental data.
Drag polars computed by the current code are compared with Tinoco’s results in
Fig. 3
; there is good agreement in the pressure drags, while the calculated skin friction drags differ. Thus, the difference between the total drag coefficients is due to discrepancies in the values of skin friction drag. The pressure differences near the shock locations have little effect on the aerodynamic coefficients. Relative to Tinoco’s results, the total drag in the
kω
SST model is lower, while the total drag in the
qω
model is higher. The differences in the total drag at
C_{L}
=0.5 are within ± 10 drag counts. The three drag polars do not show significant differences for positive incidences.
 3.2 DLRF6 Configurations
Changes in aerodynamic characteristics with respect to turbulence models are explored through simulations of the
DPWW1 geometry and pressure coefficients with the positions of η (AOA : 0.5°)
Comparison of drag polars of DPWW1
flows around the DLRF6 WB configuration. The compared turbulence models are the
qω
model, the
kω
SST model, and variations of the SST model. The Mach number is 0.75 and the lift coefficient is 0.5 under the design cruise condition. The simulations are performed at a fixed angle of attack, because matching the angle of attack provided more accuracy than matching the lift coefficient in most of the DPW results. The Reynolds number based on the MAC length is 3.0× 10
^{6}
. The medium (3.9 million grid points) and the fine (8.9 grid points) grid systems made by Tinoco
[1]
are used in the simulations. Magnified views of the grid systems near the wingbody junction are depicted in
Fig. 4
.
Figure. 5
compares the drag polars obtained with the two grid systems. In the figure, the skin friction drag, the pressure drag, and the total drag are plotted. As can be seen in
Fig. 5
, the shapes of the drag polars do not change as the grid density increases. The points on the drag polars computed with the fine grid system at an AOA lie on the lines generated with the medium grid system. Since the performance of an aircraft depends only upon the shape of the drag polar, it is decided that the subsequent computations of the DLRF6 WB are performed with the medium grid system only.
Surface grids of medium and fine grid systems near the wingbody junction (WB)
Change of drag and lift by grid resolutions (WB, AOA : 0.5°)
The DLRF6 WB configuration and the pressure coefficient distribution along the surface are presented in
Fig. 6
. The span locations of the pressure distributions are also displayed. The figure shows that the shock is clearly established on the upper surface of the wing. The angle of attack and the drag coefficients of the present results and experimental work at the design cruise condition are listed in Tab. 1. A centerline and scatter limits are also presented. The median (Med.) and the scatter limits are computed by statistical methods and used for comparison of the codetocode scatter
[11]
. The limits (upper limit : Up., lower limit : Lo.) provide a reasonable estimate of the population mean and the standard deviation of the core solutions. The table shows that the present results fall within the limits. The
qω
model generates a drag coefficient closer to the experimental results than the
kω
SST model does. The
kω
SST model tends to underestimate the drag coefficient, as in the DPWW1 results. To understand the basis of this tendency, further investigation of the behavior of turbulent viscosity and boundary layer transition is needed.
DLRF6 WB configuration and pressure coefficients with the positions of η (AOA : 0.5°)
Angles of attack and values of drag at the design cruise condition (AOA : deg, Drag: counts)
Angles of attack and values of drag at the design cruise condition (AOA : deg, Drag: counts)
Comparisons of the pressure distributions at the two span positions are presented in the
Fig. 7
. At
η
=0.331, the pressure distributions of the present results agree well with Tinoco’s numerical results and with the experimental data. At
η
=0.514, the shock computed by the
kω
SST model is slightly further downstream than those of other results, whereas the pressure distributions agree well with experiment, except on the upper surface near the shock position. The differences of the shock positions are not significant in comparison with the results obtained at the DPW. The
qω
model provides a more accurate estimate than the
kω
SST model, as can be seen in
Fig. 7
.
In
Fig. 8
, the drag polars are compared with other numerical results and with experimental data. The drag polars of the pressure drag exhibit little differences between the sets of results, but the skin friction drag shows a larger difference, demonstrating that the turbulence model significantly influences predictions of aerodynamic characteristics. The lowest calculated total drag value is from the
kω
SST model ? 23 drag counts below the experimental result at the design point; the highest is from the
qω
model ? 4 drag counts above the experimental result at the design point. The drag polars computed with the
kω
SST models, shown in
Fig. 9
, show little difference; the largest difference of total drag at the design point is 2 drag counts. Therefore, the effect on aerodynamic coefficients of the variations of the
kω
SST model is insignificant.
Simulations of the WBNP configuration are conducted under the same flow conditions as the WB configuration using Tinoco's medium (6.2 million grid points) and fine (8.7 million grid points) grid systems. Magnified views of the grids on the surface near nacelle and pylon are shown in
Fig. 10
. The changes of drag and lift using the two grids are shown in
Fig. 11
. The same movement in the drag polars is found with the fine grid system as in the WB configuration. The configuration of the WBNP and the pressure distributions around the wing are depicted in
Fig. 12
. The two
η
locations where the pressure distributions are depicted are also displayed. In
Fig. 12
, shock becomes clearly established on the upper surface of the wing. In
Fig. 13
, pressure distributions at the two locations are shown in comparison with the experimental data and Tinoco’s computational results. The results with the
qω
model correlate well with the other results at the two locations. However, the results obtained using the
kω
SST model show discrepancies in the pressure distribution on the wing’s lower surface at
η
=0.331 and near the shock position at
η
=0.514. In the most simulations, the
kω
SST model predicts that the shock would be located slightly further downstream from the shock positions from other results. More detailed study is required to identify the cause of the difference.
The streamtraces on the upper surface of the wing near the wingbody junction are shown in
Fig. 14
. The flow region with the
kω
SST model is larger than that with the
qω
model. Similar is observed in the simulations with the fine grid system and the flow separation regions are expanded as grid density increases.
Figure 15
compares the streamtraces on the lower surface of the wing near the pylon and the streamlines obtained from the oil flow experiments
[1]
. The
kω
SST model predicts an excessive flow separation in comparison with the
qω
model and the experimental data. Such a tendency has been reported in other investigations
[12

16]
. Computational results containing AOA matching using the fine grid system show similar discrepancies in the pressure distribution. Similar excessive flow separations around the lower surface of the wing near the pylon are observed with the fine grid, while the solutions obtained using the medium grid correlate well with the experimental data
[12
,
13]
. The lift matching results also show similar discrepancies in the separation bubble
[14

16]
.
Figure 16
compares the pressure distributions of previous studies and the present results. According to Klausmeyer’s numerical study
[17]
, the separation bubble size on the lower surface of the wing as well as on the wing sideofbody grew as grid density increased. According to May et al.
[18]
, the separation bubble size on the wing sideofbody was influenced by the turbulence model. The separation bubbles predicted by the SA model and the
kω
SST model were larger than those predicted by the
kω
model and the modified
kω
Comparison of pressure distributions at two span locations (WB, AOA : 0.5°)
Comparison of the drag polars with other results (WB, Large blue circle : Experiment)
Comparison of drag polars according to various forms of the SST Model (WB)
model. These results suggest that simulation under a certain combination of grid density and turbulence model results in excessive flow separation on the wing surface near the pylon.
The drag polars are compared to Tinoco’s results and experimental data in
Fig. 17
. As in DPWW1 and WB, significant differences are not observed between the pressure drags, but the skin friction drag coefficients differ from Tinoco’s results. Thus, the turbulence model would be the greatest influence on the drag coefficient in the all simulations. The skin friction drags are underestimated by the
kω
SST model in comparison with Tinoco’s results, while the skin friction drags are overestimated by the
qω
model. In comparison with the experimental data, the total
Surface grids of medium and fine grid systems near the nacellepylon (WBNP)
Changes of drag and lift with grid resolution (WBNP, AOA : 1.0°)
Comparison of pressure distributions at two span locations (WBNP, AOA : 1.0°)
drag coefficients obtained from the
kω
SST model correlate well for negative incidences, but total drag coefficients with the
qω
model are well matched for positive angles of attack.
DLRF6 WBNP configuration and pressure coefficients distributions (AOA : 1.0°)
Comparison of streamtraces near wingbody junction (WBNP, AOA : 1.0°)
Comparison of streamtraces on the wing lower surface near pylon (WBNP, AOA : 1.0°)
Comparison of pressure distributions of WBNP (η=0.331)
4. Concluding Remarks
Simulations of aircraft configurations were performed using various turbulence models to understand an aircraft’s aerodynamic characteristics. The
qω
turbulence model, the
kω
SST turbulence model, and various versions of the SST model were used. The code was verified and validated against previously attained numerical results for flows around the DPWW1 model. Numerical simulations of the DLRF6 WB and WBNP configurations were also performed and aerodynamic characteristics were compared with experimental and other computational data. In simulation
Comparison of drag polars of WBNP
of the WB configuration, the
kω
SST model underestimated skin friction drag while the
qω
model overestimated skin friction drag. Effects on the aerodynamic characteristics induced by variations of the
kω
SST model proved to be insignificant. In the WBNP configuration simulations, the total drag coefficients computed with the
kω
SST model correlated well with the experimental data for negative incidences. In contrast, the total drag coefficients obtained from using the
qω
model were well matched for positive angles of attack. The reason for the excessive flow separation resulting from the use of the
kω
SST model will be the subject of future work.
URL: http://aaac.larc.nasa.gov/tsab/cfdlarc/aiaadpw/ Workshop2/
URL: http://aaac.larc.nasa.gov/tsab/cfdlarc/aiaadpw/Workshop3/
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