ANSYS CFD – Things To Look Out For
March 5, 2017 | Mohammed S Rehman
ANSYS is a very powerful and extensive simulation software with a capability to conduct virtually any kind of simulation accurately. For a successful simulation all one has to do is to take care that the boundary conditions and the numerical models chosen replicate all conditions of the intended application of the design.
For our design purposes we conducted virtual wind tunnel tests and the conditions were replicated in the Fluent module of ANSYS. For wind tunnel tests we are interested in the region where the air will flow and not in side the pod model, thus the region of air flow must be a solid body which is meshed to solve for given conditions.
Defining the solid region in the Design Modeler of ANSYS is a relatively easy task. The profile of the tube has to be extruded first. The pod design can then be imported as a STEP/IGES file. The next step is to perform a Boolean
subtraction of the pod from the tube which will result in a solid body consisting of only the region of air flow and a void in place of the pod. Furthermore, in these wind tunnel tests the body being tested that is the pod stays stationary and the air is made to flow against it, thus extra care has to be taken to interpret the boundary conditions in order to get a result comparable to the intended application.
For our design we considered the length of the tube track to be infinitely long as the ratio of the pod length to the track length is very high and such an assumption also makes the setup easier. When running in the tube at distances, far away from the front and the back end of the pod, the pressure would be 4000 Pa. The gradients in pressure/density of the air would be observed only in the vicinity of the pod. Thus, the mesh has to be long enough to accommodate the differences and gradients of the air pressure and density up till this finite length. Local refinement is necessary near the pod regions to get accurate results of the lift and drag forces acting on the pod’s surface.
ANSYS gives a wide range of turbulence models to choose from. The K – Omega SST turbulence model with compressibility effects enabled replicates the real-world conditions of running a pod inside a tube most closely. The Far Field Pressure inlet and outlet options replicate the infinite pressure of 4000 Pa along the tube. Conducting trial simulations with many of these models along with different viscosity models, we arrived at the following boundary conditions implemented in ANSYS that are usually used for Transient Compressible flows:
Finalizing the boundary conditions was a challenging task for the team. Even with a coarse mesh, each trial simulation took around 6 to 8 hours to converge to a solution, sometimes with positive results and sometimes with garbage results. One of the mistakes we did initially was using a pressure inlet and a velocity outlet at the ends of pod to achieve the required pod velocity. This may work at nominal speeds, but at higher velocities, this condition does not give the accurate results. This assumption was confirmed as the simulated results with this boundary condition did not match the calculated results. At this point we definitely needed help. Our faculty advisor at ASU, Dr. H.P. Huang, gave us valuable insights over the turbulence models to choose from for simulating a high speed compressible flow, and how to reduce the wake formation behind the pod. These insights helped us to greatly improve our result accuracies.
With these conditions implemented, over 15 different shapes were iterated to get a shape which gave a smooth flow at different speeds. Theoretically, we expected to achieve a maximum speed of around 200m/s with our final design. Below shows an image of our first design results. This design was quite large in comparison with the tube as the initial subsystems to be placed inside were not optimized.
Additionally, mesh size also plays an important role. Finer the mesh, more precise are the results. How fine the mesh can be greatly depends on the availability of computational resources. Being slightly limited in computational power, we ran the simulations with an overall mesh size of around 4 million nodes. Reasonably accurate results were obtained with this mesh size. We also have plans to validate these results by running a series of Wind Tunnel tests at ASU.
Evolution Of Shape Designs – Eat Design Sleep Repeat
March 4, 2017 | Gurupkar S Nerwal
Eat Design Sleep Repeat! That was all we did for a month straight. We had our startup shape ready but its aerodynamic efficiency although pretty good was just not good enough to satisfy some of us. So we set out to decrease the coefficient of drag to as much as we could. We found out cd values of some famous automobiles calculated in there working environments and decided not to stop till we get better values for our pod design. Although we iterated through 15 different pod design shapes, a few of them are shown below.
The first design, as presented in the Preliminary Design Briefing, had a separate body beneath the primary pod
shell for integrating several of the pod subsystems, with each body generating its own drag forces. As shown in analysis to the left, significant vorticities formed at the tail of the pod. Although the airflow over the the top of the pod was less than Mach 1, the drag created by this shape was very high due to these vortices. The Cd achieved for this shape was 1.35 at a velocity of 150m/s.
Next our team wanted to see what happens if we take the skies out from the startup shape. So, by performing the
simulation with only the primary pod shell from shape 1, a substantial improvement in the Cd of the shape was observed, encouraging exploration of designs where all systems are integrated into a single body. The Cd for this shape dropped to a staggering 0.65 at a velocity of 150m/s.
Motivated by the results of the previous iteration, shape 3 was designed with all systems integrated in a single body.
Although the coefficient of drag increased considerably, it was determined that this was due to the shape of the pod and that further refinement and analysis could reduce the Cd. The value of Cd achieved for this shape was 2.95 at a velocity of 150m/s.
For shape 4, both the nose and the tail of the pod were grounded. This shape had a lower drag than the shape 3,
however, it was much higher than the goal of our design study. The reason for the high Cd was primarily due to the large number of vorticities that formed near the tail of the pod. This was quite an eye opening result and helped us create our next test design. The value of Cd achieved for this shape was 2.13 at a velocity of 150m/s.
Learning from our previous designs, in this design we combined the shapes form 3 and 4. To create shape 5 we
used the nose from shape 4 and the tail form shape 3. The Cd of this shape dropped considerably to 1.4 from 2.13 and 2.95 at a testing speed of 150m/s, thus encouraging us further to develop more designs and test new and different shapes.
Shape 6 was our first break through. We were able to achieve a Cd of 0.21 at 150m/s. This shape was inspired from the design of an aerofoil. To keep the length of the pod within reasonable limits, we had to reduce the height
considerably. The CFD results for this design were so encouraging that we decided to take our pod speed to the next level. Although the results at 200m/s and 250m/s gave us Cd of 0.4 and 1.69, we had to get a good Cd at least at 200m/s. After 200m/s, the flow speed of the air above the pod exceeded the Kantrowitz limit, inducing a shock waves, and causing a large increase the Cd. This being our major hindrance we targeted our next design to overcome this limit.
After few more design iterations, we finally zeroed in on a refined version of the shape 6. At 200m/s, this variation of shape 6 experiences a total aerodynamic drag of 86 N with a coefficient of drag of 0.26 and a coefficient of lift of -0.01. The velocity of air over the top of the pod stays
well below Mach 1 thus keeping the Kantrowitz effect in check. The total volume of this pod shape is 1.02 m3 and
has sufficient space for integration of subsystems into the pod hull. The pressure range of air around the pod is well under safe operating limits and no significant formations of vorticities are created behind the pod. The temperature on the pod surface and of the air around the pod due to aerodynamic forces is less than 320 K. Thus, the temperatures generated by the drag are within safe functional limits.
The graph below shows the evolution of the ten shape iterations. The coefficient of drag ranged from 0.21 to 2.95 for an air velocity of 150 m/s, finally settling on design 10. The final design has a drag coefficient of 0.26 at an air velocity of 200 m/s.
The Startup Shape – Think Think Think
March 4, 2017 | Gurupkar S Nerwal
How do you start? That is the first question that hits you when faced with the challenge of creating an absolutely new shape with a very high aerodynamic efficiency. A lot of new ideas would be a good start. I guess one could argue that a new idea is absolutely unique with no connections to other existing ideas, but I believe that all new ideas are built on top of existing ideas. Existing ideas are what fuel the creation of new ones. That is how we started our journey for achieving a highly efficient aerodynamic shape.
Being in the second competition, we had access to data from a large number of teams that participated in the first competition. We researched those team’s data and noted down all the pros and cons of their shape designs paying attention to even the slightest of details and variations.
This research helped us learn a lot before even starting our design. With the help of this research we created our first design. This design was pretty similar to the already existing designs. The main body was cylindrical in shape With a large converging nose and a thin converging tail. The main body was also supported by a second body at the bottom which we call the ‘skies’. The skies acted as a housing for all the braking and levitation systems. These skies were designed to maintain lateral stability, provide optimal braking efficiency and to maintain contact with the track through out the run. The ski all in all provided a neat and efficient housing for the sub systems but it was a cause of a large amount of aerodynamic drag. On running computational fluid dynamic analysis on the pod shape we discovered that there was non uniform air flow at the tail of the pod which created a wake region due to which the aerodynamic drag experienced on the pod increased considerably.
After a few minor variations and very small improvements in drag values, we submitted this design as a part of our preliminary design package to SpaceX. When our design was approved by SpaceX to enter the next stage we knew that we had a lot of work ahead of us and we will have brainstorm a lot to completely redesign our shape to get even better values of aerodynamic drag in order to win this competition.