Hybrid Laminar Flow Control Activities within the Frame of Clean Sky 2
Bibliographic record
Abstract
Reducing the environmental footprint of passenger aircraft is one of the most important challenges the aircraft industry is facing today. A decrease of fuel burn and consequently also CO2 plays an essential role in defining next generation aircraft. Obviously, any means to lower cruise drag are to be explored, where Hybrid Laminar Flow Control (HLFC) is one promising option. By influencing the boundary layer of a wing section in a way that delays the transition of the flow from laminar to turbulent, i.e. by increasing the laminar flow wing surface region, a significant drag reduction can be achieved. Within the Clean Sky 2 programme HLFC activities were focused on maturing existing HLFC principles with the goal to provide solutions which can be easily adapted for industrial needs of aircraft manufacturers. During the last eight years the HLFC activities concentrated on developing and maturing manufacturing technologies for HLFC up to a Technology Readiness Level (TRL) 5/6. Moreover, an innovative and simplified HLFC system was elaborated and its system integration into a wing was demonstrated with a final maturity of TRL 4. This paper aims to give an overview of the work performed and describe the chosen design approach to achieve an overall solution for an HLFC wing. In the end, the final overall solution and its key features is explained on a ground-based demonstrator manufactured within the programme.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".