SACCON CFD Simulation (Compared by Wind Tunnel Data)

     This post is about the CFD analysis of the SACCON UCAV. Designed by NATO’s (North Atlantic Treaty Organization) RTO (Research and Technology Group) under Applied Vehicle Task Group (AVT-161) to assess the performance of military aircraft. The aircraft Geometry is shown in Fig. 1. The aircraft geometry is available here [1].

Fig. 1, SACCON UCAV

The aircraft flight parameters and dimensions are given in [1]. The simulations are validated with published literature [1]. SolidWorks Flow Simulation Premium software is employed for the simulations. Fig. 2 shows results of Cl, Cd and Cm at various angles of attack. It can be seen that the results are agreement with the published experimental data.

Fig. 2, Comparison of simulation results

The mesh has 3.7 million cells in total. Special mesh refinements are added in the regions of interest i.e. regions with high gradients, the wake and on the control surfaces of the aircraft. The computational domain and the mesh for 16° angle of attack is shown in Fig. 3.

Fig. 3, The computational mesh and domain

The mesh has 3.7 million cells in total. Special mesh refinements are added in the regions of interest i.e. regions with high gradients, the wake and on the control surfaces of the aircraft. The computational domain and the mesh for 16° angle of attack is shown in Fig. 3.

Fig. 4, CFD post processing

Thank you for reading, If you would like to collaborate on projects, please reach out.

References

[1] Andreas Schütte, Dietrich Hummel and Stephan M. Hitzel, “Flow Physics Analyses of a Generic Unmanned Combat Aerial Vehicle Configuration,” Journal of Aircraft, Vol. 49, No. 6, December 2012, https://doi.org/10.2514/1.C031386

Fifth Generation Fighter Aircraft CFD Simulation (Backed-up by Wind Tunnel Data)

     This post is about the CFD analysis of a Sydney Standard Aerodynamic Model (SSAM-Gen5) in flight at various angles of attack. The SSAM-Gen5 model is based on the Lockheed Martin F-22 Raptor. The aircraft Geometry is shown in Fig. 1. The aircraft geometry is available here and here. Machine Learning can be read here.

Fig. 1, SSAM-Gen5 CAD

     The aircraft flight parameters and dimensions are given in [1]. The simulations are validated with published literature [1]. SolidWorks Flow Simulation Premium software is employed for the simulations. Fig. 2-4 shows results of C_l, C_d and L/D at various angles of attack. It can be seen that the results are in close agreement with the published experimental data.

Fig. 2, A comparison of coefficient of lift

Fig. 3, A comparison of coefficient of drag

Fig. 4, A comparison of coefficient of lift to drag ratio

     The mesh has 796,327 cells in total. With 68,630 cells on the aircraft surface. Special mesh refinements are added in the regions of interest i.e. regions with high gradients, the wake and on the control surfaces of the aircraft. The mesh for 15° angle of attack is shown in Fig. 5.

Fig. 5, The computational mesh

     The results from the CFD post processing are presented next. Velocity iso-surfaces showing pressure distribution around the aircraft, surface pressure distribution on the aircraft and vorticity in the direction of flight at the wake of the aircraft are shown in Fig. 6. Within Fig. 6, the black arrows represent the direction of on coming flow. The angle of attack for Fig. 6 is at 15°.

Fig. 5, The post processing

     Thank you for reading, If you would like to collaborate on projects, please reach out.

[1] Tamas Bykerk, Nicholas F. Giannelis and Gareth A. Vio. "Static Aerodynamic Analysis of a Generic Fifth Generation Fighter Aircraft," AIAA SCITECH 2022 Forum, 2022-1951, 2022 doi.org/10.2514/6.2022-1951

Heaving Airfoil Simulation

This post is about a 2D NACA 0012 heaving aerofoil. Heaving motion is achieved by changing the angle of attack on the aerofoil based on the Eqn. 1.

α_e = arctan[2*π*St_a*cos(2*π*f_h*t)]+ α_i               Eqn. 1

w.r.t. Eqn. 1, α_e is the effective angle of attack, St_a is Strouhal number, f_h is the heaving frequency.

The case S1 and H6 from [1] are compared in the animations below.

If you want to collaborate on the research projects related to turbomachinery, aerodynamics, renewable energy, please reach out. Thank you very much for reading.

References

[1] https://doi.org/10.1121/10.0001419

Flying Wing Design using Computational Fluid Dynamics (Verification and Validation)

     This post is about a transient simulation of the ONERA M-6 flying-wing aircraft with a cross-section of ONERA D airfoil, as shown in Fig. 1.

Fig. 1, The simulated geometry. 

     The mesh had 2,474,614 cells in total with 300,892 cells on the wing surface, as shown in Fig. 2. The computational domain is shown in Fig. 4. The computational domain walls are at a distance equal to ten times the wingspan.

Fig. 2, The computational mesh.

Fig. 3, The computational domain.

     The simulated conditions are taken from [1-4] i.e. a freestream Mach number of 0.8395 at 101,325 Pa and 293.2 K. The angle of attack is 3.06°.

     The lift force from the present numerical simulation is at 19,551.65 N as compared to the lift force of 20,438.53 N as determined by [1-4]. The numerically determined drag force from present simulation is 1,370.88 N as compared to 1,313.24 N, as determined by [1-4].

     The results of lift and drag force from the present simulation are within 4.35% and 4.2% of the results calculated by NASA [4] and [1-3]. Streamlines and pressure surface plot around the aircraft surface are shown in Fig. 4.

Fig. 4, Results.

References

[1] Le Moigne, "A Discrete Navier-Stokes Adjoint Method for Aerodynamic Optimization of Blended Wing-Body, Configurations", PhD thesis, Cranfield University, United Kingdom, 2002.

[2] J. Lee, C. S. Kim, C. Kim, O. H. Rho, and K. D. Lee, "Parallelized Design Optimization for Transonic Wings using Aerodynamic Sensitivity Analysis", AIAA Paper 2002-0264, 2002.

[3] E. J. Neilsen and W. K. Anderson, "Recent Improvements in Aerodynamic Design Optimization on Unstructured Meshes", AIAA Paper 2001-0596, 2001.

[4] 3D ONERA M6 Wing Validation, https://turbmodels.larc.nasa.gov/onerawingnumerics_val_sa.html.

     Thank you for reading. If you would like to collaborate on research projects, please reach out.

Computational Fluid Dynamics Analysis of a Drone

     This post is about the computational fluid dynamics analysis of a small drone. The drone features a blended body-wing design with various cross sections at different span-wise locations. The drone has a wing-span and length of 6 ft. and 4.92 ft., respectively. The root (center) portion of the drone is relatively thicker and symmetrical in cross section for increased mechanical strength while the the mid-section and wing tips are thinner and utilize more cambered aero-foils. This is purely a concept design and as of now, no physical model of this drone exists.

     The numerical simulations for the present study were carried out using SolidWorks Flow Simulation Premium© code. The code employs κ-ε model with Two-Scales Wall Functions approach as the turbulence model. The numerical algorithm implemented is the SIMPLE-R, modified. The second-order upwind discretization scheme is used to approximate the convective fluxes while the diffusive terms are approximated using the central differencing scheme. The time derivatives are approximated with an implicit first order Euler scheme. The SolidWorks Flow Simulation© solves the Navier-Stokes equations, equations 1-3, which are formulations of mass, momentum and energy conservation laws for fluid flows. Turbulent flows are predicted using the Favre-averaged Navier-Stokes equations.

     The mesh independence test was carried out starting with 348,679 fluid cells. The mesh density was then increased up to 2,360,514 cells. The results of mesh independence study are mentioned below.

                          Mesh Name             Cells            Lift [N]         Drag [N]      Lift/Drag

                          M1                          348,679       382.41 -48.14      7.95

                          M2                          1,032,665    466.08      -48.73      9.57

                          M3                          1,559,516    473.48 -47.89      9.89

                          M4                          1,990,010    486.38 -48.08      10.12

                          M5                          2,360,514    491.07 -48.32      10.16

     It can be seen that as the mesh density increased, the difference in the critical parameters between two successive meshes also reduced. The mesh independence test was stopped as the difference between all of the critical parameters was less than one percent for the meshes M4 and M5.

     The pressure and velocity plots at various span-wise locations are shown in Fig. 1-2. It can be clearly seen that there is a negligible change in the velocity and pressure distributions around the drone between meshes M4 and M5. It can also be seen that as the mesh becomes finer, the resolution of both the pressure and velocity plots also increases.

Fig. 1, Velocity contours of various meshes.

Fig. 2, Pressure contours of various meshes.

     Aero-acoustics around the drone were also examined, as shown Fig. 3.

Fig. 3, Sound level contours of various meshes.

     A zoomed in view of the computational mesh is shown in Fig. 4. The refined mesh at the drone walls as a result of the mesh controls employed is clearly visible. The hump near the root of the drone is also visible, it was added in order to prevent the span-wise flow.

Fig. 4, Mesh level M4.

     The boundary conditions and the computational domain are shown in Fig. 5. The large red arrows represents inlet velocity boundary condition and the large blue arrows represents the atmospheric pressure outlet boundary condition. The red squares represents real wall boundary condition (slip) applied to the computational domain walls so that the boundary layer from the walls does not effect the flow around the drone.

An animation of an aileron roll can be seen here.

Thank you for reading. If you would like to collaborate on research projects, please reach out.

High Camber Wing CFD Simulation

     This post is about the numerical simulation of a high camber, large aspect ratio wing. The wing had an aspect ratio of 5:1. The Reynolds number of flow was 500,000. The wing was at an angle of attack of zero degree. The aero-foil employed had a cross section of NACA 9410.

     The software employed was Flow Simulation Premium. A Cartesian mesh was created using the immersed boundary method. The mesh had 581,005 cells. Among those 581,005 cells, 55,882 were at the solid-fluid boundary. A time step of ~0.00528167 s was employed*. The domain was large enough to accurately trace the flow around the wing without any numerical or reversed flow errors. The software employs κ-ε turbulence model with damping functions, SIMPLE-R (modified) as the numerical algorithm and second order upwind and central approximations as the spatial discretization schemes for the convective fluxes and diffusive terms. The time derivatives are approximated with an implicit first-order Euler scheme.

     The mesh is shown in Fig. 1. The four layers of different mesh density are also visible in Fig. 1, the mesh is refined near the wing surface using a mesh control. The velocity around the wing section is shown in Fig. 2, using a cut plot at  the center of the wing. In Fig. 2, the wing body is super imposed by pressure plot. The velocity vectors showing the direction of flow are superimposed on both the wing body and the velocity cut plot.

Fig. 1, The computational domain.

Fig. 2, The velocity and pressure plots.

     The results of the simulation was validated against the results from XFLR5 software. XFLR5 predicted slightly higher lift and slightly less drag on the wing for same boundary conditions because the XFLR5 simulations were inviscid.

     Thank you for reading. If you would like to contribute to the research, both financially and scientifically, please feel free to reach out.

     *Time step is averaged because of the fact that a smaller time step was employed at the start of the numerical simulation.

Computational Fluid Dynamics Analysis of a Symmetrical Wing, Update 01

     This post is about the computational fluid dynamics analysis of a wing. The wing analyzed employed the NACA 0021 section throughout. The wing had a span of 4 m and a chord length of 1 m. The Reynolds number was kept at 3,000,000. The software employed was SolidWorks Flow Simulation Premium.

     The mesh had a total of 385,064 cells of which 84,826 cells were in contact with the wing surface, as shown in Fig. 1. The results are, indeed, mesh independent. Mesh controls were employed to refine the mesh near the wing surface. The computational domain employed was of cylindrical shape.

 

Fig. 1, The computational mesh around the wing.

 

     The velocity variation at various angles of attack around the wing cross-section is shown in Fig. 3 while the pressure variation on the wing surface is shown in Fig. 4. The results were validated against experiments conducted by [1].

 

Fig. 2, Velocity variation around the wing at 0-25 degree AOA, 5 degree increments.

 

Fig. 3, Pressure variation at the wing surface at 0-25 degree AOA, 5 degree increments.

     The purpose of this blog is maintain my online portfolio. I did this analysis because I realized I haven't written anything of this nature before. All of my previous simulations and/or blog entries were from the propulsion, renewable energy and turbo-machinery areas.
 

     Update 01

     CAD files are available here.

 

    
     Thank you for reading. If you would like to collaborate on research projects, please feel free to contact.

     [1] Fernando A. Rocha, Adson A. de Paula, Marcos d. Sousa, André V. Cavalieri, and Vitor G. Kleine, "Lift enhancement by wavy leading edges at Reynolds numbers between 700,000 and 3,000,000," Proceedings of the 2018 Applied Aerodynamics Conference, AIAA AVIATION Forum, Atlanta, GA, 2018.

     https://doi.org/10.2514/6.2018-3816