Deriving Sensitivities of Distributed Propeller Propulsion in Overwing Configuration by using DoE in CFD Simulations
Bibliographic record
Abstract
In this publication the interaction of propeller and wing of a short haul aircraft concept with distributed propeller propulsion in an over-wing configuration at cruise conditions is investigated. The numerical studies are performed in the CFD solver TAU of the German Aerospace Center using the Actuator Disk together with a wing segment with constant chord length. All calculations are performed in trimmed condition, i.e. with constant lift and drag coefficients. The resulting propeller power is used as a comparative variable. In order to quantify the influences of the position in chord direction, distance to the wing surface, installation angle, propeller diameter as well as distance between the propellers, a design of experiments approach with face centered central composite design is used. The resulting regression model is shown and analyzed. The procedure is carried out with two different propeller designs. In the first run, a propeller designed for a conventional aircraft with one propeller per half span is used. In the second run, a propeller designed for a distributed propulsion concept facing lower loads is used. The changes in the regression models are shown and compared. It hast been shown that the chord position has a significant influence on the performance of the propeller. The lowest power is reached when the propeller is positioned above the trailing edge of the wing. In addition, there is a dependence on the installation angle, which roughly follows the surface curvature of the airfoil. For the distance to the top of the wing, only a negligible effect on the propeller performance was found. For a positioning above the trailing edge, the propeller diameter and the spanwise distance between the propellers were found to be dominant parameters. Even when using a different propeller design, the linear dependence remains. For both propellers investigated, there is a similar optimal combination of diameter and spanwise distance with lowest power.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".