Wake-Model Effects on Induced Drag Prediction of Staggered Boxwings
Bibliographic record
Abstract
For staggered boxwings the predictions of induced drag that rely on common potential-flow methods can be of limited accuracy. For example, linear, freestream-fixed wake models cannot resolve effects related to wake deflection and roll-up, which can have significant affects on the induced drag projection of these systems. The present work investigates the principle impact of wake modelling on the accuracy of induced drag prediction of boxwings with stagger. The study compares induced drag predictions of a higher-order potential-flow method that uses fixed and relaxed-wake models, and of an Euler-flow method. Positive-staggered systems at positive angles of attack are found to be particularly prone to higher-order wake effects due to vertical contraction of wakes trajectories, which results in smaller effective height-to-span ratios than compared with negative stagger and thus closer interactions between trailing wakes and lifting surfaces. Therefore, when trying to predict induced drag of positive staggered boxwings, only a potential-flow method with a fully relaxed-wake model will provide the high-degree of accuracy that rivals that of an Euler method while being computationally significantly more efficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".