Design of Next Generation Civil and Military Aircraft with Ultra-High Bypass Engine using Composites, Advanced Materials and Technology
Bibliographic record
Abstract
Indirect combustion noise had not been attracting research in the past, but recent indication seems to prove that it could be a threat in the future if not addressed. Means of reducing this type of noise to a low decibel value was also included. Noise is due to the ingestion of distorted atmospheric turbulence, as the two set of blades rotate in different direction. Open rotor noise is higher since the rotors are fully exposed to oncoming turbulence and lack ducting or a nacelle to attenuate the radiated sound. A thorough review on the technology that can replace conventional turbofan was carried out. It was found that none of this technology can meet up with the ACARE and NASA 2020 vision but left a gap to be filled. Because open rotor is the most proven engine that is able to satisfy this requirements, different methods are adopted and integrated to reduce open rotor noise. Attention was paid to the geometry of the blade, hub and blade length, the vorticity and interaction noise are simulated until an optimized blade was achieved. The integration problem of open rotor was addressed where the engine was located to minimize perceive noise to the payload.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".