Aerodynamic Design and Analysis of a Formula SAE Drag Reduction System (DRS)
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Formula SAE vehicles, like many other vehicles within motorsport, often employ rear mounted aerodynamic devices to improve cornering performance, these devices can however have a significant amount of aerodynamic drag. Additional speed can be gained by reducing the impact of the rear wing on the straightaways of the track through the use the aptly named Drag Reduction System (DRS), which works by reducing the angle of attack of the rear wing flap(s).</div><div class="htmlview paragraph">A DRS can however introduce other performance losses, including the losses from having a gap between the rear wing flaps and endplate to prevent friction, the potential to stall the rear wing from improper opening angles of the flaps, and from the wake of the DRS actuator if positioned in front of the airfoils. An additional concern is the time it takes for the rear wing performance to return upon DRS deactivation, which will affect how long before corner entry the driver must disable the system.</div><div class="htmlview paragraph">Insight into each of these problems as well as the optimum opening angles was found through the use of CFD using Siemens’ STAR-CCM+ 2019.1. Simplified geometry came from UMSAE Polar Bear Racing’s car, PBR20, out of the University of Manitoba. All steady state simulations were done using RANS, while the DRS deactivation study was done using a novel method using Detached Eddy Simulation (DES), where dynamic overset meshes were used to model the transient motion of the flaps. As a result of the deactivation study, new insight was gained into the dynamic behaviour of drag reduction systems.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".