Aerodynamic Design and Analysis of a Formula SAE Drag Reduction System (DRS)
Bibliographic record
Abstract
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). 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. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".