Verification and Validation of Transonic Flow Over

A 3D ONERA Wing using ANSYS

Syed Waqas Ali Shah                                                  Mehmood Khan                                                        Asadullah Jan

Abstract—  ONERA-M6  wing  is  a  well-known  aero  foil geometry created in the 70’s as it is one of the most well-known experimental aerodynamic cases. Because of its simple shape, it


is associated with the complexities of transonic flow. Since its acceptance  as  a  validation  case  in  numerous  CFD  research








articles, it has effectively formed a standard for CFD programs

code  validations  cases.    The  ONERA  M-6’s  wing  has  been examined for transonic flow using ANSYS Fluent. On the wing,

the   location   of   shock  waves   and   the   supersonic   area   are computed. The Spalart-Allmaras Turbulence Model was used to compute  a  3D  flow  simulation  on  the  ONERA  M6  wing  in

Fluent®.  At  an  angle  of  attack  of  3.06  degrees,  the  flow  was modelled   as   transonic   and   compressible,   with   a   Reynolds

0.8395                    11.72E+06             3.06

The  M-6  wing  of  the  ONERA is  a  swept,  semi-span  wing with no convolution [4]. The following are its specs:


number of 11.72e+6 and a Mach of 0.8395. The CFD findings was  validated  with  the  NASA  experimental  data  for  the  1/5th span region of the wing.



Mean aerodynamic chord


Leading edge sweep (degree)

Trailing edge sweep (degree)

Taper ratio

Keywords—Onera   M-6   wing,   transonic   flow,   Reynolds number, Mach number, Angle of attack.


The transonic regime has been intensively explored and investigated in the field of flying. The development of shock waves,  the  interactions  of  turbulence  boundary  layers,  and other   factors   all   affect   transonic   flight,   causing   flow separation  and  large-scale  instabilities.  The  transonic  flow over  the  ONERA  wing  has  been  studied  through  various experiments, wind tunnels and computational analysis. The Boeing  777,  747,  and  other  subsonic  aircrafts cruise  at  a speed of 0.85 Mach. [1] CFD analysis has recently become the  most  precise  method  of  calculating  airfoil  and  wing properties.   Computational   Fluid   Dynamics   (CFD)   is   a cutting-edge  technique  for  using  numerical  simulation  to solve    real-world    issues    in    fluid    dynamics    including compressible   and   incompressible   fluids   thanks   to   latest powerful  computers.  In  1972,  the  ONERA  Aerodynamics department designed  a ONERA M6  wing. It  was based  on experimental geometry for examining high Reynolds number and three-dimensional flows under a variety of multilayered, complex   flow   conditions   [2],   fairly   simple   geometry, sophisticated     flow     physics,     and     experimental     data accessibility. This testing condition is carried out in inviscid flows with a transonic Mach number.


Nowadays,   the   flow   field   phenomena   are   used   in   the modelling of Computational Fluid Dynamics, as indicated in Table 1. The table displays the details of flow conditions at a high Reynolds number of 11.72 million, which is based on a mean aerodynamic chord of 1.1963m and air is considered to be an ideal gas [3].

1.1963           0.64607               30               15.8            0.562

The CFD findings will next be compared to experimental data for the 1/5th span region of the wing. The CFD findings can also  be  compared  to  the  WIND  ®  simulation  results  from NASA.


  1. Geometry

ANSYS  Design  Modeler®  is  used  to  the  Onera  M6  wing geometry.  Figure  1  depicts  the  ONERA  M6  wing’s  3D geometry, which was produced with the data points available from NASA WIND simulation in ANSYS Design Modeler.

Figure 1 Onera M-6 Wing Geometry


Figure 2 Shows the wing’s enclosure/flow-domain, including the inlet, outlet, and far and symmetry sides.

Figure 2: The wings enclosure for computational setup

  1. Mesh

Mesh is extremely important for computational applications. The meshing is done in such a way that the entire domain is covered  and  there  is  no  empty  space  between  the  cells. Furthermore,  there  are  no  negative  volumes  in  the  domain and  no  cells  overlap  each  other.  A  high-quality  mesh  can produce superior results, allowing for better comparison with available  experimental  or  CFD  findings  for  validation  and verification. Structured meshing is chosen over unstructured and  hybrid  meshing  because  the  structured  discretization system discretizes the boundary surface of the flow domain using a quadrilateral called the surface grid and captures the entire geometry with  hexahedral  [5]. Stable connectivity is used  to  display  structured  meshes.  [6]  In  two  dimensions, quadrilateral    adoptions    are    possible,    while    in    three dimensions, hexahedral adoptions are possible. This is a very efficient use of space, i.e. The region associations are then well-defined by the storing technique. [7]

Mesh details are displayed in table 3


Type             Number of Nodes          Number of Elements

Mesh                95867                       337965

Figure 4 shows refinement of mesh near the wing

Figure 4: Mesh Refinement

  1. Boundary Conditions

Provide  inlet  and  outlet  boundary  conditions  as  the  next assignment   to   solve.   These   conditions   offer   the   inlet condition for the solver to flow (inlet pressure). In addition, we can give inlet velocity, inlet mass flow rate, or a Cartesian or cylindrical velocity component. As indicated in Table 4 [8] outlet  conditions  are  also  provided  at  the  outlet  boundary, where we can set exit static pressure or exit mass flow rate.


Boundary Name              Boundary Type                   Condition

Near side                                  Symmetry              Symmetrical w.r.t boundary

Wing surface                   Wall                                No-slip condition

Inlet Outlet Far side

Pressure far field             T(R) = 460 K Pressure(psi)=




Where the fluid properties are given below


Fluid                                              Properties

air                         Density: (as of Ideal gas) Viscosity:1.09329e-05 lb./ft.s-1


Figure 3: Mesh of Wing’s flow Domain

  1. Solver & Turbulence Model

A pressure-based solver with a pseudo-transient method was utilized to solve the problem, with Spalart-Allmaras (1eqn) as  the  turbulence  model.  This  S-A  turbulence  model  was created specifically for aerospace applications that are  wall constrained and have high pressure gradients. [9]

  2. Verification:

Lift coefficient (Cl) and drag coefficient (Cd) CFD findings from ANSYS fluent can be verified using NASA CFD data from WIND® programme. As a result, Table 6 compares the Fluent CFD findings to the NASA CFD software results. [10]

Cd                   Cl                  %error




NASA Result Fluent Result

0.0088               0.1410

0.01106495    0.126495     20.4%          10.2%

The   pressure   coefficient   that   curves   on   the   wing   was displayed  in  Figures  5  to  7.  Figure  6  illustrates  a  distinct shock development as well as an analytical comparison of Cp contours  at  the  wing’s  symmetry  plane  with  NASA’s  mesh CFD [11].

Figure 5: Pressure Coefficient Contours(Fluent)

Figure 6 Nasa pressure Contours

Figure 7: pressure coefficient symmetry contours

Furthermore, Mach number contours at the symmetry plane were generated to visualize the effect of the boundary layer, as  shown  in  Figure  8.  It  demonstrates  that  there  is  a  thin boundary layer in the highlighted region, which thickens after the shock. [12]

Figure 8:  Mach Number symmetry Contours

  1. Validation of CFD Results:

To compare Cp (Pressure coefficient) results at this place on the wing with the existing experimental data, a polyline was created at a span wise location of 0.2 ft. Figure 10 depicts Cp’s CFD results in an upright configuration based on the experimental data available at this location, which supported the findings.

[8    P. M. A. A. G. J. Dandois, “Buffet Cheracterization

]              and Control for Turbulent Wing,,” 2013.

[9  P. D. P. d. P. G. Sébastien Deck ∗, “Development and

Figure 9- Pressure coefficient along wing surface at 1/5th span wise location


The research reveals that the Transonic ONERA wing has reasonable    favorable    computational    outcomes.    The outcome   shows   a   high   level   of   agreement   with   the experimental data. The good agreement between the CFD result of the Cp distribution and the experimental data is highly encouraging. The S-A turbulence model was used to run a 3D flow simulation on the ONERA M6 wing. At an AOA of 3.06 degrees, the flow was modelled as transonic and  compressible,  with  a  Reynolds  number  of  11.72e+6 and a Mach of 0.8395 [13].

  1. As seen   in   figure   8,   the   wing   experiences supersonic  conditions,  a  shock,  and  boundary layer separation.
  2. As shown  in  Figure  9,  the  CFD  results  were confirmed and demonstrated excellent agreement with the available experimental data.

]        application of Spalart–Allmaras one equation,”

Aerospace Science and Technology 6 (2002) 171–

183, 2002.

[1    J. Slater, “Demonstrate computation for a 3D wing

0]                                   flow,” 2002.

[1    N. N. Q. Durrani, “Comparison of RANS, DES and

1]     DDES Results for ONERA M6 Wing at Transonic

Flow,” 2011.

[1    I. B. H. Saïdi, “Numerical simulations of the shock

2]            wave-boundary layer interactions,” 2019.

[1    F. K. D. A. A. S. R. T. D. Palacious, “Analysis and

3]      Design Technology for Turbulent Flows,” 2014.

[1              M. Haenggi, Boeing widebodies, 2003.

] [2  D. McBride, “A coupled finite volume method for the

]     solution of flow processes on complex geometries,”


[3  “

]                     g/m6wing01/m6wing01.html”. [4                            “”.

] [5           J. Blazek, “Principles of Grid Generation”.

] [6      Lambropoulos, “Acceleration of a Navier Stokes

]         Equation Solver for Unstructured Grods using

Agglomeration Multigrid and Parallel Processing,”


[7         K. O. S. S. t. R. S. Chitale, “Boundary Layer

]      Adoptivity for Tranosnic Turbulent Flows,” 2013.

Leave a Comment