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 5×10 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.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".