An Aerodynamic Study and Design Methodology for the 2009 Supermileage Body
Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) has gained recognition as a valuable simulation tool for many industrial applications, creating immense opportunities for introducing CFD into standard undergraduate curriculum. This paper is focused on an extensive aerodynamic CFD study related to the 2009 Supermileage Vehicle. Supermileage Competition is a trademark competition conducted by the Society of Automotive Engineers (SAE). This paper provides an aid for future undergraduate students and young researchers interested in designing low drag vehicles. The major part of this study includes airfoil selections and three dimensional CFD iterations to optimize the body design. The methodology focuses on an inside-out approach to optimization of body shape through computation of aerodynamic forces on a low mass vehicle. The 2008 Supermileage car, after four design iterations, had a drag coefficient of 0.16. The 2009 body, in its final full body design, has a drag coefficient equal to 0.12, which is the lowest drag coefficient ever calculated for a University of Windsor Supermileage car. Due to the current economic state and reduced funding, an additional low budget vehicle was also designed and extensive aerodynamic studies were conducted to validate the design. The new low budget design includes a scoop at the leading edge of the vehicle and the CFD model included a driver model for accuracy. After several design iterations the low budget design yielded a drag coefficient of 0.14. Due to the weight savings, conforming to SAE rules, and considering the current economic conditions, the low budget design was finalized for the 2009 Supermileage Vehicle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".