A Sensitivity-Driven Approach to Automotive Aerodynamic Design
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3387.vid This paper investigates the use of an expert-driven, sensitivity-based approach for automotive aerodynamic design. The continuous adjoint flow fields with respect to drag are used to provide additional information on how the momentum around a body contributes to its drag. A Momentum Contribution Field is defined, where areas of high positive and negative momentum contribution to drag are isolated and used to qualitatively guide design modifications to the body. Application of this approach is demonstrated using unsteady computational fluid dynamics models of the DrivAer estateback at 120 km/h to evaluate the design modifications. Results of these analyses predict that an 8.8% reduction in the vehicle’s drag was achieved through the modifications inspired by the Momentum Contribution Field. The drag tends to be reduced through mitigation of the splitter’s leading-edge separation, improved shielding of the front tires, better management of the flow coming off the rear fenders, and an acceleration of the flow along the roofline and spoiler.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".