CFD Analysis of Automotive Bodies in Static Pressure Gradients
Bibliographic record
Abstract
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 and add more realistic model and wind tunnel conditions to the evaluations of the performance of the two-measurement technique. The subject CFD studies are designed to greatly reduce all wind tunnel interference effects except for the variation of the non-linear static pressure gradients. A zero gradient condition is generated by simulating a solid wall test section with a blockage ratio of 0.1%. The non-linear gradients are simulated using a semi-open jet test section with a very large 40 square meter nozzle exit and a variable length test section. Under these conditions, the variation in drag coefficient is observed with and without application of the two-measurement method adjustments. Conclusions are reached relative to the ability of this approach to achieve interference free results on a fully detailed sedan body style and the sources of the drag change caused by the pressure gradient.
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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) | 0.002 | 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".