Effects of an Underbody Device on the Characteristics of a Trailer Truck Wake Region
Bibliographic record
Abstract
The aerodynamics of bluff bodies and flow separation are encountered in many industrial applications. Flow separation causes significant pressure fluctuations that can yield undesirable effects such as vibration, noise, and drag. It is well-known that at highway speeds, over 50% of the fuel is used by a road vehicle to overcome aerodynamic drag. Due to these reasons, bluff body aerodynamics has been the subject of intensive research interests for many decades. In this paper, a new concept of an underbody aerodynamic device is used to modify the turbulent wake region of a bluff body. In particular, the underbody device was designed in order to allow for the recirculating flow to reattach and exit the underside of the bluff body while increasing the average speed of the flow and preventing side winds from disturbing the flow. This significantly reduces the underbody recirculation zone, which is a major source of drag. In addition, this ensures that the flow exits with minimum turbulence to reduce the size of the bluff body’s wake. The studies were conducted using the RANS based turbulence model, k-ω SST in ANSYS Fluent. A width-based Reynolds number of 1.1 × 106 was used to conduct the simulations in order to validate the baseline model with NASA’s wind tunnel data; which include the surface pressure coefficients and a drag coefficient. The paper focuses on the changes in the model’s wake that were introduced due to the device and their influence on the underside flow. The results showed that the device significantly reduced the recirculation at the underside of the bluff body. This was found to increase the coefficient of pressure at the base of the model, which reduced the size of the wake. These changes in the flow field resulted in an overall drag coefficient reduction of 4.1%.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".