STUDY OF THE INTERACTIONS BETWEEN TAILPIPE EXHAUST FLOW AND AHMED BODY WAKE
Bibliographic record
Abstract
Air quality is a key issue. Cars contribute significantly to the emission of gaseous pollutants. It is crucial to analyse how these pollutants disperse in the wake of a vehicle and infiltrate the car cabin exposing passengers to high concentrations. Here, a 3D numerical study of the flow developing downstream of a simplified car model (squared-back Ahmed body) is presented when a gas (Nitrogen) is emitted from the exhaust pipe. A Reynolds-Average-Navier-Stokes (RANS) model (k- SST) is used coupled with a multicomponent gas method. Parameters for the simulation correspond to experimental investigations led in a wind tunnel to allow comparisons and the validation of the results. Based on the height of the car and the incoming velocity, the Reynolds number is 510 4 . Altogether, these conditions correspond to an urban environment. This study focuses on the mixing between air and nitrogen and the flow dynamics in the close wake. We show that the tailpipe flow has no significant influence on the wake flow meaning that it is fully offset by the momentum of the incoming flow. Comparisons with experimental data obtained in wind tunnel at the same reduced scale are provided. Strong agreements are found for mean and turbulent velocities and for Reynolds stresses validating our model. The results of the volume fraction of nitrogen are also discussed indicating that the gas tracer is captured by the recirculation region due to turbulent structures. These results could provide interesting indications regarding the positioning of air intake in order to minimize pollutant infiltration.
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".