The Process of Making an Aerodynamically Efficient Car Body for the SAE Supermileage Competition
Bibliographic record
Abstract
In the summer of 2010, a new body shell for the SAE Supermileage car of Laval University was designed. The complete shell design process included, amongst other steps, the generation of a shape through the parametric shape modeling software Unigraphics NX7 and the evaluation of aerodynamic forces acting on the chassis using the open source Computational Fluid Dynamics (CFD) software OpenFOAM. The CFD analyses were ran at steady-state using a k-omega-SST turbulence model and roughly 2.5 million cells. An efficient method for evaluating the effect of ambient wind conditions and vehicle trajectory on the track was developed. It considers the proportion of time that the car operates at each combination of velocity and wind yaw angle and computes the overall energy demand of the shell. An iterative process was conducted over a significant number of different shapes, which were generated by joining formula-based guide curves using intersection and tangency conditions. The new shell has a 25 % larger frontal area due to modified design constraints. When aerodynamically compared to the smaller and already highly efficient old vehicle, reductions of 50 % of the negative lift, 15 % of the energy demand when driving forward, and 5 % of the energy demand when turning are achieved by the new design. Also, the drag coefficient is reduced by 20 %. These improvements come from the quasi-NACA profiles on the side and top walls; a reduction of cavities to prevent redundant frontal areas; a short vehicle and smother wheel cover closures; and a thorough study of the nose and tail. This paper describes numerical flow simulations and the changes that were brought to the vehicle body to make it as aerodynamically efficient as possible.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".