Bibliographic record
Abstract
Distributed electric propulsion (DEP) is an emerging set of technologies which enable new vehicle configurations by allowing the efficient distribution of many smaller propulsors around the airframe. One application of this technology is to greatly enhance the short takeoff and landing (STOL) capability of a fixed-wing aircraft. STOL aircraft may have advantages over vertical takeoff and land (VTOL) configurations being considered for urban passenger transport missions due to lower risk associated with the certification process and improved performance or reduced weight due to smaller required propulsion systems. To be useful for these missions, STOL vehicles require short-field performance competitive with vertical lift configurations. One pathway to achieving this is by placing many electric motors and propellers along the leading edge of the wing, an arrangement referred to as a DEP blown wing. This arrangement increases the effective lift of the wing through interaction of the propeller slipstream with the trailing edge flap. Previous blown wing concepts, based on large propellers or turbine engines, were mechanically complex and adopted only for specialized applications. A DEP blown wing offers a simpler and potentially more efficient way to enhance the high-lift capability of a wing, but the performance is not reliably predictable using existing theoretical or empirical methods. A wind tunnel test of a representative 2D blown wing section was undertaken, and section lift coefficient values up to 9 were measured at moderate power settings. The results of this wind tunnel testing were used to predict the takeoff and landing performance of reference vehicles with wing and power loading representative of modern GA aircraft. The results of this analysis suggest that a DEP blown wing may enable takeoff and landing ground rolls of less than 100 ft. Landing distance over a 50 ft obstacle is identified as the likely driver of runway requirements for a super-short takeoff and landing vehicle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".