A Simple Passive Device for the Drag Reduction of an Ahmed Body
Bibliographic record
Abstract
In this paper, a simple passive device is proposed for drag reduction on the 35° Ahmed body. The device is a simple rectangular flap installed at the slant surface of the model to investigate the effect of slant volume, formed between the device and the slant surface, on the flow behaviour. The slant volume can be varied by changing the flap angle. This investigation is performed using the FLUENT software at a Reynolds number of 7.8 ×〖10〗^5 based on the height of the model. The SST k-omega model is used to solve the Navier-stokes equations. It is found that this passive device influences the separation bubbles created inside the slant volume and provides a maximum drag reduction of approximately 14% at the flap angle of 10°. Moreover, the device delays the main separation point, which changes the flow conditions at the back of the model. The drag reduction was found to mainly dependent on the suppression of the separation bubbles formed inside the slant volume, which leads to faster pressure recovery. The cause of this pressure recovery is found to be the reduction in recirculation length and width. Also, the addition of a flap reduces the turbulent kinetic energy, which lessened the wake entrainment in the recirculation region, leading to a drag reduction. Also, it hinders the formation of horseshoe vortex that provides a pressure recovery and influence the wake width. However, the investigation also reveals that this device does not reduce the induced drag due to longitudinal vortex from the side edges.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".