Design of an Actively Controlled Aerodynamic Wing to Increase High-Speed Vehicle Safety
Bibliographic record
Abstract
This paper presents the design of airfoil and briefly introduces a real physical prototype for an actively controlled wing to improve high speed vehicle safety. Conventionally, active safety systems of road vehicles, including active steering and differential braking, mainly manipulate the tire/road forces to enhance the lateral stability of vehicles. However, this active safety technology is hindered by the saturation of tire/road forces at high lateral accelerations and on icy slippery roads. In contrast, the use of controlled aerodynamic forces has received little attention. In this paper, the actively controlled wing is proposed to manipulate the negative lift force (downforce) to enhance handling capabilities of vehicles at high speeds. Various wings are examined in terms of airfoil shapes, coefficient of drag and lift, resulting yaw/roll moments, effect of wing attack angle at different Reynolds numbers using numerical simulations with X-Foil, Gambit and Fluent software packages. A prototype of the proposed actively controlled wing is briefly introduced. The essential design parameters, functionality and features of the active wing are addressed. In a companion paper that will follow, the stability control analysis of the vehicle with the actively controlled wing will be offered.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".