Influence of Wheel Drive Unit Belt Width on the Aerodynamics of Passenger Vehicles
Bibliographic record
Abstract
Wind tunnels are an essential tool in vehicle development. To simulate the relative velocity between the vehicle and the ground, wind tunnels are typically equipped with moving ground and boundary layer control systems. For passenger vehicles, wind tunnels with five-belt systems are commonly used as a trade-off between accurate replication of the road conditions and uncertainty of the force measurements. To allow different tyre sizes, the wheel drive units (WDUs) can often be fitted with belts of various widths. Using wider belts, the moving ground simulation area increases at the negative cost of larger parasitic lift forces, caused by the connection between the WDUs and the balance. In this work, a crossover SUV was tested with 280 and 360mm wide belts, capturing forces, surface pressures and flow fields. For further insights, numerical simulations were also used. It was found that the belt width can substantially alter the flow field, mainly locally around the wheels but also away from the belts. The drag coefficient increased by approximately 0.010 CD for the narrow belts, mainly due to differences in the vortex structures downstream of the front wheels, resulting in larger low-energy regions for the narrow belts. Numerical simulations showed that the change in vortex structures was caused by leakage flows from the gaps between the WDUs and the stationary floor. Comparing two rim designs, it was found that the belt width did not only affect the absolute force measurements but also the drag delta between them. Although the parasitic lift forces increase with the wider belts, a correction is necessary regardless. Hence, given the fact that the interfering leakage flows are further away from the tyres with the wider belts, they should be the preferred choice, allowing more accurate investigations of wheel aerodynamics.
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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 | 0.001 |
| 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 | 0.000 | 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 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".