Detached Eddy Simulation of the 28° Ahmed Body at a Low Reynolds Number
Bibliographic record
Abstract
Abstract Studies on the Ahmed body with varying slant angles remain an interesting topic, especially analyses of the flow structures and the corresponding changes in aerodynamic drag and lift in the region 25° ≤ α ≤ 30°, which are not fully understood. Physical insights in the aerodynamic performance of a simplified geometry such as the Ahmed body can improve the geometric optimization of road vehicles for fuel economy improvement. Therefore, this paper examines the three-dimensional wake dynamics of a 28° slanted Ahmed body and proposes a flow control method for its drag reduction. This slanted angle is rarely reported in the open literature. The study is conducted by applying the improved delayed detached eddy simulation (IDDES) using the SST k-ω turbulence model to solve the Navier-Stokes equations at a low Reynolds number of 1.4 × 104 based on the model height. The results reveal a flow separation at the slant surface and reattachment at the rear leading to a secondary separation. A small reverse flow develops after the first separation over the slant surface. Similarly, another minor reverse flow region is concentrated in the middle of the vertical base. However, the flow control method modifies the flow structures similar to the low-drag regime Ahmed body. The aspect ratio of the recirculation region is increased, and the reattachment at the rear end vanishes. Consequently, even at the low Reynolds number studied here, the drag is reduced by up 11%. In addition, the study employs both the time-averaged and time-resolved turbulence statistics and vortex identification methods to provide physical insights into flow modifications and drag reduction. Hence, the paper provides additional valuable information on the flow structure at low Reynolds number to the body of knowledge.
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) | 0.001 | 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 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".