CFD Analysis of Various Automotive Bodies in Linear Static Pressure Gradients
Bibliographic record
Abstract
Establishing data adjustments that will give an interference free result for bluff bodies in automotive wind tunnels has been pursued for at least the last 45 years. Recently, the Two-Measurement correction method that yields a wake distortion adjustment for open jet wind tunnels has shown promise of being able to adjust for many of the effects of non-ideal static pressure gradients on bluff automotive bodies. Utilization of this adjustment has shown that a consistent drag results when the vehicle is subjected to the various gradients generated in open jet wind tunnels. What has been lacking is whether this consistent result is independent of the other tunnel interference effects. The studies presented here are intended to fill that gap on the performance of the two-measurement technique. The subject CFD studies are designed to eliminate all wind tunnel interference effects except for the variation of the (linear) static pressure gradient. Zero gradients and linear gradients are generated by tapering the walls of solid wall test sections with a blockage ratio of 0.5%. Under these conditions, the variation in drag coefficient is observed with and without application of simple buoyancy adjustments, Glauert Factor based adjustments, and the two-measurement method adjustments. Conclusions are reached relative to the ability with each of these approaches to achieve interference free results on a range of vehicle body styles both simplified and with a moderate level of production vehicle detail.
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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 | 0.000 | 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".