Investigation and Development of Underbody Aerodynamic Drag Reduction Devices for Trailer Trucks
Bibliographic record
Abstract
It is well known that the underbody region of a tractor-trailer is responsible for up to 30% of the aerodynamic drag. This is the highest drag created by any region of a tractor-trailer. There are a number of underbody drag-reduction devices available on the market but they create a few operational issues, such as low ground clearance and ice collection, which inhibit their mass market appeal. In this paper, a novel concept of an underbody aerodynamic device is developed and investigated. The underbody device is a combination of a ramp and a side skirt; which are optimized simultaneously. In addition, the device is made collapsible to facilitate easy storage when not in use (i.e., city driving). NASA’s Generic Conventional Model (GCM); a 1/8th scale model of a generic class-8 tractor-trailer is used to evaluate and optimize the concept. The GCM allows the concept to be applicable to a wider range of tractor-trailers. The studies were conducted using the RANS based turbulence model, k-ω SST in ANSYS Fluent. The simulations were validated with NASA’s experimental data on the GCM model; which include the surface pressure coefficients and a drag coefficient of the model. The results showed that the underbody device decreased the overall drag coefficient by 4.1%. In addition, the adverse negative pressure region in the wake was significantly reduced.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".