9. Simulation
9.8. Results
9.8.1. Acceleration event
The acceleration event is a 75 m straight line sprint with the score being based on elapsed time.
The simulation predicts a time of 3.849 s will be required to complete the sprint, with a speed of 32.08 m/s at the finish line. The distance covered is shown plotted against time in Figure 9.11.
Unlike the circuit events, it is simple to compare this time against previous results since the acceleration event is the same at every Formula Student venue. The points are calculated with respect to the fastest time, which in 2014 was 3.439 s achieved by ETH Zurich [4]. The formula used for the acceleration score is equation (9.12) [5], where Tyour is the achieved time, Tmin is the
elapsed time of the fastest car and Tmax is 150% of Tmin. The acceleration score that would have been achieved in 2014 by the simulated time of 3.849 s is 52.15, out of a possible 75 points. This would have corresponded to 3rd place in the acceleration event [4].
Acceleration score=
71.5× (
TyourTmax−1 )
Tmax Tmin
−1
+3.5 (9.12)
Figure 9.11. Shows the time taken to complete the 75m acceleration event
9.8.2. Autocross event (single lap)
The autocross event is a single lap of the track from a standing start. Up to 100 points are scored on how quickly the lap is completed. For the allocated circuit (see Chapter 1), the simulation predicts an achievable lap time of 37.83 s, with an average speed of 26.96 m/s and a maximum speed of 38.62 m/s. Figure 9.12 shows the velocity over the single lap, with the driver’s control demands also plotted where a value of 10 corresponds to maximum demand.
Figure 9.12. Velocity and driver demands over the course of one lap from a standing start
9.8.3. Endurance event
The endurance event is worth the most points of all events (300) and consist of 27 laps of the track. Therefore the majority of optimisation work that made use of the simulation was carried out using the results from the endurance lap time. The simulation predicts an achievable lap time of 36.05 s with an average speed of 28.30 m/s and a top speed of 39.19 m/s. This suggests a total time for the endurance event would be 16 minutes and 15 seconds. Figure 9.13 shows the velocity over a typical lap of the endurance event, the driver’s control demands are again plotted where a value of 10 corresponds to maximum demand. The reduction in mass due to fuel burn was not taken into account on a lap by lap basis because it was shown to only cause a 30 ms difference in lap time, which makes less than a second of difference over the course of the event. However a reduced mass figure was used for the simulation of the much shorter acceleration and autocross events.
Figure 9.13. Velocity and driver demands over the course of one lap which follows a previous lap
9.9. Conclusion
The simulation has proved invaluable throughout this design project as it allows the trial of different setups with feedback within a few seconds. This has allowed for educated decisions to be made by every team member, based on what forces and speeds the car is undergoing, and how alterations affect lap times. Finally, the simulation has provided results for the dynamic events for the final iteration of design (see Table 9.3), allowing for comparison among other design projects and, in some cases such as the acceleration event, previous Formula Student competitions. It is difficult to compare the autocross and endurance event results with previous competitions due to the
allocation of a new track design for this project. Therefore a valuable piece of future work would be to use the simulation to produce results for the tracks used in previous competitions. As for future developments to the simulation, the next step would be to model each component of the drivetrain individually with their own velocity states. This would provide a more accurate representation of the drivetrain and allow for some improved decisions in certain areas, such as differential choice, where a lack of real world experience (or more accurate simulation) made the decision difficult.
The results proved most sensitive to the chosen cornering speeds, much more so than any single aspect of the car’s physical specification. Therefore in order to improve on the results in Table 9.3
further investigation could be made into how the car may be able to make best use of the track space as opposed to assuming each corner will be driven at constant radius. Furthermore, the simulation could be developed to allow for braking during corners. For fuel consumption figures it is necessary to consult Section 2.4.11, which has made use of the simulated throttle demand.
Table 9.3. Simulation results for the three time tested dynamic events
Event Result Time / Points
75 m acceleration test Time 3.849 s)
Points scored (2014) 52 out of 75 points (3rd place)
Autocross sprint Lap time 37.83 s
Top speed 38.62 m/s
Average speed 26.96
Endurance (27 laps) Endurance time 975 s
Fastest lap 36.05 s
Top speed 39.19 m/s
Average speed 28.25 m/s
9.10. References
[1] Optimisation of the chain drive system on sports motorcycles, 2004, Burgess and Lodge [2] www.zeroshift.com/pdf/ Seamless%20AMT%20Offers%20Efficient%20Alternative%20To
Everyone working on this design project was doing so alongside numerous other commitments, thus effective project management was key to ensuring effective teamwork. Meetings were held twice weekly where possible, and means to communicate and share as a group were established using the internet for times when meeting in person was not possible, or for between meeting communications. Strict attention was paid to the project’s budget throughout. In the early stages cost estimates were shared within the team by means of an online spreadsheet, which provided everyone with a clear picture of the then rapidly changing financial situation.
10.2. Project planning
The project spanned two eight week terms (during which the team could meet in person) and two five week breaks (during which most of the team could not meet in person). There was a fixed