Vehicle Architecture Synthesis
Please enjoy the 2-minute summary gallery below, or scroll further to explore my work in greater detail
The vehicle architecture, in the context of this project, defines the platform around which the rest of the subsystem components are designed. This includes the overall vehicle dimensions, as well significant packaging and layout decisions. Since the architecture rests as the starting point for the rest of the vehicle design, it is critically important that it aligns with our overall design goals and ideology.
The vehicle architecture synthesis takes place as the first step in the overall project that was the 2020 vehicle design tradespace analysis. You can read more about that project here. In this page, I will walk you through the two optimization loops that were carried out to maximize a vehicle design concept, as well as the supplementary analyses to ensure secondary goals were met. Moving forward, this vehicle architecture will serve as the launching point for the Clemson Formula SAE team's newest development platform.
Starting Point Caveats
When generating a vehicle architecture tied to a driving design ideology, two of the most important variables are the tire and engine selection. This is especially true for Formula SAE, where there are several types of concepts allowed in the technical regulations. Correctly matching an engine, tire and vehicle concept is critical if you are approaching from a clean slate.
For our team however, it was practical to carry over our existing infrastructure and use that as a starting point. This includes using the Honda CBR600rr 4 cylinder engine. Our team has invested so much time and resources into this engine, including things such as spare parts, Ricardo WAVE models, and dyno stand components, that changing engines would be unjustified given our financial limitations. In addition, our knowledge of the design and tuning decisions that best suit the engine took years to develop, and it would be hard to properly evaluate other engines to the same degree.
The CBR600rr, for its many virtues, suffers from high weight compared to its alternatives. Combining this with our manufacturing capabilities (a carbon monocoque is still a few years out, for example) sets a rough floor estimate for a minimum achievable vehicle weight. This, in turn, affects our tire selection, and was a factor in the decision to use the Hoosier R25B (more information here).
In summary, it would have been ideal to start with a clean sheet for this project, but given the time and resource limitations of the team it was better to use the current engine+tire combination as a launch point.
My vehicle architecture exploration took place within the previously mentioned tradespace analysis framework. Analysis was carried out inside ModeFRONTIER, creating a central environment to link the relevant subsytem models together. My quasi-steady state lap time simulation was used to evaluate design concepts, with the primary objective to maximize dynamic event points scored. The vehicle model used was a two-track bicycle model with aerodynamics, leaving out suspension kinematics and other higher order effects while accounting for weight+load transfer distribution. This took place over three phases. The first phase is the "Conceptual Optimization", where vehicle dimensions and mass properties were free, uncoupled variables. This allowed for an exploration of the isolated effects of the parameters in question, to start to inform potential design targets. The second phase was the "Parametric Optimization", where vehicle parameters and the subsequent mass distribution properties were coupled using the Solidworks parametric chassis model. This allowed me to constrain my optimization to feasible and realistic solutions. The final step is a supplementary analysis of vehicle response and stability metrics, to help inform a final decision.
For the conceptual optimization, I explored the effects of weight distribution, track width, and final drive ratio on vehicle performance, using the single objective to maximize dynamic event scores. This is reflected in the ModeFRONTIER workflow below, illustrating the inputs (green), outputs (blue), and optimization objectives (blue arrow). All optimizations began with a pseudo-random Sobol sequence, with starting points equally distributed within the design space. Final optimization was carried out using a Moga-II genetic optimization routine.
Final Drive Ratio
First I'd like to discuss the gearing (final drive) exploration. Our team adjusts final drive ratio by changing the engine output sprocket. I included it in this analysis because of the potential to impact aerodynamic design targets and torque delivery as they relate to rear grip and straight line acceleration. Due to the intake restriction regulation in Formula SAE, however, our car was power limited for the majority of its operating range, even with a more powerful 4-cylinder engine. As a result, final drive results turned out much more straightforward.
The plot below outlines the final drive effects on the straight-line acceleration event alone. The shortest gearing configuration (blue, 11 tooth sprocket) is faster, so long as the rearward weight distribution is high enough. Otherwise, at around 58% front weight, the 12 tooth sprocket (green) overtakes it with more traction off of the line.
Acceleration event score (y) versus front weight distribution (x), color coded by the number of teeth on the engine sprocket.
However, once on the track, the results turn to favor the 12 tooth sprocket. The plot below summarizes configurations explored with all three sprockets, and the subsequent autocross and endurance event scores. The 11 tooth sprocket, despite the improved acceleration, increased the necessary amount of gearshifts around the course, hurting overall performance. This is supported by final drive testing results the previous year, where drivers preferred the 12 tooth sprocket for the same reason. They also disliked the 13 tooth sprocket (the tallest configuration available) for a lack of accessible torque at low speeds.
Endurance event score (y) versus autocross event score (x), color coded by the number of teeth on the engine sprocket.
Interestingly enough, when comparing the 11 and 13 tooth sprockets, the 11 tooth performs better on the endurance course, while the 13 tooth performs better on the autocross course. When comparing the course maps, the autocross course is far more flow-y and slalom-heavy, while the endurance course features more significant acceleration and braking zones. These areas are where the car benefits more from an added acceleration boost, which helps explain the performance split.
Next I will discuss the results pertaining to weight distribution. Whenever possible, I like to preface my optimization routine with an independent design exploration, to make sure the expected vehicle behaviors are being captured. This also helps build an intuition framework from which to view future optimizations. The following plot explores the effect of weight distribution on individual acceleration mode performance:
These results line up with the intuition that a 50/50 weight distribution favors cornering performance, loading the front and rear tires equally, and that a rearward weight distribution favors acceleration and braking performance. As speed increases, these benefits in longitudinal acceleration become less pronounced due to less torque on the rear axle and downforce having a more dominant effect on tire loading.
Intuition would suggest the optimal weight distribution depends on the relative importance of cornering and acceleration capacity, and finding the right balance between the two. Below, you can see the iteration trace of the ModeFRONTIER optimizer converging to a final weight distribution of around 46.5% front, bringing the best combined scores from the skidpad, acceleration, autocross, and endurance event.