Aerodynamic Performance of Natural Laminar Flow Aerofoils Applied to Low- and High-speed Wings
Bibliographic record
Abstract
The aim of this investigation is to assess if the use of Natural Laminar Flow (NLF) aerofoils alone improves the efficiency of a wing in comparison to their NACA equivalents.To compare performance, the lift-to-drag ratio (Efficiency) of the wings has been considered.A cruise speed range from M0.1 to M0.3 was analysed for the low-speed wing, while a Mach sweep between M0.3 and M0.85 was assessed for the high-speed wing.A final analysis was performed to evaluate the effect of the presence of a sweepback angle in the wing, from straight leading edge to a 10-degree-sweepback wing.For the low-speed wing, it was observed that the efficiency of the laminar wing is slightly decreasing with speed (up to 17% between M0.1 and M0.3) but is increasingly higher than in the NACA wing (from 5% at M0.1 to 16% at M0.3).This means that the aim of the laminar aerofoil is met, so that for a cruise speed between 100 and 300km/h lower drag is produced and therefore lower thrust (and fuel consumption) is required.In the case of the high-speed application, it was found that the laminar flow wing had lower efficiencies when compared to its NACA equivalent.Analysing the results, it was noted that the ratio of the lateral forces to lift had a direct relation to the efficiency: when this ratio was increased, the efficiency was decreased, and vice-versa.It was observed that, in the case of laminar wings, not applying a sweepback to the leading edge (LE) is optimal and duplicates the efficiency with respect to adding any angle.Moreover, this is the only case observed where the efficiency of the laminar wing is higher than its NACA equivalent.
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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 | 0.001 |
| 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 | 0.000 |
| 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".