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

Computational Fluid Dynamics Simulation of a Swimming Fish (Includes UDF)

      This post is about the simulation of a swimming fish. The fish body is made of NACA 0020 and 0015 aero-foils (air-foils). The fluke is made of NACA 0025 aero-foil (air-foil), as shown in Fig. 1. the CAD files with computational domain modelled around the fish is available here.

Fig. 1, The generic fish CAD model

      The motion of the fish's body is achieved using a combination of two user-defined functions (UDF). The UDFs use DEFINE_GRID_MOTION script mentioned below, for the head/front portion. This is taken from the ANSYS Fluent software manual, available in its original form here. The original UDF is modified for present use as required. To move the mesh, dynamic mesh option within ANSYS Fluent is enabled; with smoothing and re-meshing options. The period of oscillation is kept at 2.0 s. The Reynolds number of flow is kept at 100,000; which is typical for a swimming fish.

/**********************************************************

 node motion based on simple beam deflection equation

 compiled UDF

 **********************************************************/

#include "udf.h"

DEFINE_GRID_MOTION(undulating_head,domain,dt,time,dtime)

{

  Thread *tf = DT_THREAD(dt);

  face_t f;

  Node *v;

  real NV_VEC(omega), NV_VEC(axis), NV_VEC(dx);

  real NV_VEC(origin), NV_VEC(rvec);

  real sign;

  int n;

  

  /* set deforming flag on adjacent cell zone */

  SET_DEFORMING_THREAD_FLAG(THREAD_T0(tf));

  sign = 0.15707963267948966192313216916398 * cos (3.1415926535897932384626433832795 * time);

  

  Message ("time = %f, omega = %f\n", time, sign);

  

  NV_S(omega, =, 0.0);

  NV_D(axis, =, 0.0, 1.0, 0.0);

  NV_D(origin, =, 0.7, 0.0, 0.0);

  

  begin_f_loop(f,tf)

    {

      f_node_loop(f,tf,n)

        {

          v = F_NODE(f,tf,n);

          /* update node if x position is greater than 0.02

             and that the current node has not been previously

             visited when looping through previous faces */

          if (NODE_X(v) > 0.05 && NODE_X(v) < 0.7 && NODE_POS_NEED_UPDATE (v))

            {

              /* indicate that node position has been update

                 so that it's not updated more than once */

              NODE_POS_UPDATED(v);

              omega[1] = sign * pow (NODE_X(v), 0.5);

              NV_VV(rvec, =, NODE_COORD(v), -, origin);

              NV_CROSS(dx, omega, rvec);

              NV_S(dx, *=, dtime);

              NV_V(NODE_COORD(v), +=, dx);

            }

        }

    }

  end_f_loop(f,tf);

}

      The computational mesh, as shown in Fig. 2, uses cut-cell method with inflation layers. The mesh has 2,633,133 cells. The near wall y+ is kept at 5. The Spalart-Allmaras turbulence model is used to model the turbulence. The second order upwind scheme is used to discretize the momentum and modified turbulent viscosity equations. The time-step for this study is kept at 100th/period of oscillation.

Fig. 2, The mesh and zoom in view of the trailing edge.

      The animation showing fish motion is shown in Fig. 3. Within Fig. 3, the left side showcases the velocity iso-surfaces coloured by pressure and the vorticity iso-surfaces coloured by velocity magnitude is shown on the right.

Fig. 3, The animation.

      Another animation showing the fish motion is shown in Fig.4. Within Fig. 4, the left side shows surface pressure while the right side shows pressure iso-surfaces coloured by vorticity.

Fig. 4, The animation.

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

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.

Steady-State VS Transient Propeller Numerical Simulation Comparison

     This post is about the comparison between steady-state and transient computational fluid dynamics analysis of two different propellers. The propellers under investigation are 11x7 and 11x4.7 propellers. The first number in the propeller nomenclature is the propeller diameter and the second number represents the propeller pitch, both parameters are in inch. The transient analysis was carried out using the sliding mesh technique while the steady-state results were obtained by the local rotating region-averaging method. For details about 11x7 propeller click here, for the details about 11x4.7 propeller, click here.

 

     As expected, the propeller efficiencies of transient and steady-state analysis are within 0.9% of each other, as shown in Fig. 1-2. Therefore, it is advised to simulate propellers and horizontal axis wind turbines using the steady-state technique as long as no time-dependent boundary conditions are employed.

 

Fig. 1, Propeller efficiency plot.

  

 Fig. 2, Propeller efficiency plot.

 

     It can be seen from Fig. 3-4 that time taken by the steady-state simulation to converge is on average 42.37% less that the transient analysis.  The steady-state analysis takes considerably less time to give a solution then a transient analysis.

 

Fig. 3, Solution time.

 

Fig. 4, Solution time.

 

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

SolidWorks Flow Simulation Premium: Propeller/Horizontal Axis Wind Turbine CFD Tutorial

The tutorial is in the attached video. Analysis such as: 11x7 Aeronautic Propeller Characteristics, 11x4.7 Aeronautic Propeller Characteristics and Wind Turbine SolidWorks Flow Simulation Premium Computational Fluid Dynamics are setup using the same method.

Bond Graphs from System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems (Dean C. Karnopp) Chapter Four (4.1i, 4.1j and 4.2a.)

4.1i

4.1j

4.2a

Bond Graphs from System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems (Dean C. Karnopp) Chapter Four (4.1e, 4.1f ,4.1g, 4.1h.)

4.1e

 

4.1f

4.1g

 

4.1h

 

Bond Graphs from System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems (Dean C. Karnopp) Chapter Four (4.1a, 4.1b, 4.1c, 4.1d)

4.1a

4.1b

 4.1c

 4.1d

Pipe Flow Simulation

Just ran another simulation related to HMT, this problem became steady state after about 36 seconds.

Water at 318 K starts flowing (0.00035 m^3/s) through a steel pipe initially at 298 K. The steel pipe had convection to air at 298 K at 3,000 W/m^2.K. A simple simulation yielded inner and outer wall temperatures of the pipe to be 309.07 K and 311.26 K respectively. Then I ran a transient simulation, to find out the time taken by the pipe’s walls to reach these temperatures (f...rom 298 K) as water flows through it. It came out to be around 36 seconds.

Then I ran a FEA. To calculate stresses induced in the pipe due to water pressure, thermal effects, gravity etc. The pipe’s diameter increased by 0.005866 mm and von-mises stress induced was 117,016,056 N/m^2 with a factor of safety of 5.302.

Then I ran fatigue study to see if the pipe will survive under these loads for 20 years or not. It will I think. The fatigue S-N curves were not available so I used the ones for carbon steel (slightly different from the ones I used for CFD analysis and FEA); so will it last for 20 years I am not sure yet (searching for curves).

 Temperatures at inner wall surface

 Temperature at outer wall surface

displacement and stress animation

Canal Turbine Concept

It's a concept I am currently working on, so far I gave made a CAD model (renderings attached) of it in SolidWorks and analyzed it using its built in CFD module.

There are many advantages of canal turbines over wind turbines, prominent one's being:

 

Unidirectional flow

Water flows in one direction in a canal so we don't need pitch and yaw control surfaces. That simplifies the design process and reduces weight.

Constant flow rate

We (humans) control water flow rate through canals and it's almost same all year, so we don't have to worry about blade aero foil design to suit variable/abruptly variable flow rate, that makes design process further straight forward.

Large Electricity potential

Canals are 100s of km long, imagine the electricity potential in the canals. You can put these turbines in irrigation canals and it'll power nearby villages and all the irrigation equipment etc.

Higher Power/Discharge Ratio

Water is ~816 times dense (powerful) than air, so for the same discharge (flow) rate we get potentially 816 times more power. Which means more we can make designs that are lighter, smaller and easier to manage and maintain.

Easy maintenance

Fitted less than ~1 m deep inside the canal and can be retracted for maintenance at ground level, making maintenance very easy or better yet, we can maintain them while canals are being cleaned.

Plots for Comparison between Lift and Drag Produced by a Legacy Wing VS a Wing with Tubercles (Humpback Whale Fin's Inspired)

For some reason plots are now showing up in the original post (http://3dimensionaldesigningandmanufacturing.blogspot.com/2015/07/comparison-between-lift-and-drag.html).

Comparison between Lift and Drag Produced by a Legacy Wing VS a Wing with Tubercles (Humpback Whale Fin's Inspired)

* Link for Plots (now showing here for some reason) http://3dimensionaldesigningandmanufacturing.blogspot.com/2015/07/plots-for-comparison-between-lift-and.html

Following data was obtained from the CFD Simulations carried out in SolidWorks Flow Simulation Premium.

Project: Design of a Wing/Blade with Tubercles for Airplanes and/or Turbines

Without Tubercles
Air Speed in Km/h	Lift in N	Drag in N
150	46.307	14.775
140	39.942	12.917
130	33.432	11.057
120	28.807	9.498
110	24.234	7.928
100	20.593	6.625
90	15.836	5.352
80	12.482	4.205
70	9.411	3.243
60	7.272	2.406
50	4.873	1.680
40	3.130	1.082
30	1.763	0.612
20	0.810	0.279
10	0.231	0.072

 

 

With Tubercles
Air Speed in Km/h	Lift in N	Drag in N
150	50.616	11.360
140	48.131	10.008
130	37.190	8.505
120	30.988	7.309
110	24.784	6.079
100	20.892	5.094
90	17.225	4.146
80	13.412	3.287
70	9.955	2.507
60	7.444	1.849
50	4.955	1.286
40	2.991	0.828
30	1.652	0.468
20	0.725	0.212
10	0.214	0.057

 

Comparison between Lift and Drag

Air Speed in Km/h	Percentage Less Drag		Percentage More Lift
150	23.113		8.513
140	22.520		17.014
130	23.080		10.105
120	22.974		7.038
110	23.322		2.219
100	23.109		1.431
90	22.534		8.064
80	21.831		6.934
70	22.695		5.465
60	23.150		2.311
50	23.452		1.655
40	23.475		-7.523
30	23.529		-6.719
20	24.014		-11.72
10	20.833		-7.94

 

It is clear that the wing with tubercles not only produces more lift at a particular velocity but also less drag.

Data for the Wing without Tubercles:

Wing Span: 1.07 m

Chord Length: 0.229 m

Air Velocity: 0-150 Km/h head on

Vertical Pitch: 0 Degree

Gravity Considered

Fluid: Dry Air at STP

Mesh Settings: Coarse (3/8)

Data for the Wing with Tubercles:

Wing Span: 1.067 m

Chord Length Large: 0.229 m

Chord Length Small: 0.203 m

Air Velocity: 0-150 Km/h head on

Vertical Pitch: 0 Degree

Gravity Considered

Fluid: Dry Air at STP

Mesh Settings: Coarse (3/8)

Let's now take a look at visual representation of data.

This Plot Shows Air Velocity VS Drag, Lift by the Wing without Tubercles

This Plot Shows Air Velocity VS Drag, Lift by the Wing with Tubercles

As you can see from above two plots; the wing with tubercles generates more lift and less drag.

This Plot Shows Air Velocity VS Lift Generated by the Wings

The green line represents the Lift generated by the wing with tubercles. It is between two to six percent more at each velocity.

This Plot Shows Air Velocity VS Drag Generated by the Wings

The green line represents the Drag generated by the wing with tubercles. It is around twenty two percent less at each velocity.

This Plot Shows Air velocity VS Lift to Drag Ratio

It is clear from this plot that Lift to Drag ratio of the wing with tubercles is around thirty three percent more for the wing without tubercles at a velocity point.

 

This Plot Shows Air Flow around the Wings at 150 Km/h from the Right Side

This Plot Shows Air Flow around the Wings at 150 Km/h

The Need for Tubercles

In aviation there are four forces at play, Lift which over comes Weight and Thrust which overcomes Drag. For a cruise speed at a particular altitude, three of these forces are almost constant. Our goal is to minimize Thrust, Drag and Weight and maximize Lift, this is because Thrust costs in terms of fuel flow rate and Weight and Drag negatively impacts on the agility of the aircraft. Aerodynamically efficient Wings and/or Blades with "Tubercles" will not only increase Lift and but also decrease Drag. This all means that we will need less Thrust for a cruise speed than before, that results in savings in terms of fuel which will result in healthier environment.

 

Applications:

 

Canal Turbine Concept

It's a concept I am currently working on, so far I gave made a CAD model (renderings attached) of it in SolidWorks and analyzed it using its built in CFD module.

There are many advantages of canal turbines over wind turbines, prominent one's being:

 

Unidirectional flow

Water flows in one direction in a canal so we don't need pitch and yaw control surfaces. That simplifies the design process and reduces weight.

Constant flow rate

We (humans) control water flow rate through canals and it's almost same all year, so we don't have to worry about blade aero foil design to suit variable/abruptly variable flow rate, that makes design process further straight forward.

Large Electricity potential

Canals are 100s of km long, imagine the electricity potential in the canals. You can put these turbines in irrigation canals and it'll power nearby villages and all the irrigation equipment etc.

Higher Power/Discharge Ratio

Water is ~816 times dense (powerful) than air, so for the same discharge (flow) rate we get potentially 816 times more power. Which means more we can make designs that are lighter, smaller and easier to manage and maintain.

Easy maintenance

Fitted less than ~1 m deep inside the canal and can be retracted for maintenance at ground level, making maintenance very easy or better yet, we can maintain them while canals are being cleaned.