Far-Field Drag Decomposition Method Applied to the DPW-5 Test Case Results
Bibliographic record
Abstract
A fareld drag prediction and decomposition method has been applied to the results of AIAA Drag Prediction Workshop 5 (DPW-5) held in Louisiana during the summer of 2012. The method has two principal advantages: it allows the removal of spurious drag inherent to CFD solutions, and it allows the decomposition of drag into viscous, wave, and induced physical drag components. This research shows that accurate drag coe cients can be predicted on coarse grids when the spurious drag is extracted with the fareld method, and that these results are closer to experimental values than drag coe cients computed on ner meshes when spurious drag is not extracted. The research also investigated the reasons behind the lift and drag losses found by some participants in the Workshop. It is shown that the lift loss is caused by the boundary layer separation at the wing root, inducing a reduction of 20% of the shock wave drag and a signi cant change in wing loading. The initiation of bu et is also analyzed. The study shows that mesh re nement is critical to capture the physical e ects of the ow, such as its separation, and provides an explanation of the discrepancies in results observed at DPW-5.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".