Vibration Transfer Path Analysis of Aeroengines Using Bond Graph Theory
Bibliographic record
Abstract
Low profit margins faced by the airline operator have forced the sector to seek the development of efficient aeroengines. The demand for more efficient aeroengines has been amplified with worldwide increase in prices of energy commodities. In order to meet this demand, aeroengine manufacturers have focused on developing lightweight aeroengines that use advanced lightweight materials with higher power performance and output. By developing lightweight aeroengines, the structural response and sensitivity to internal excitation loadings attributed to rotor system unbalance forces caused by mass eccentricity is amplified and increased [1]. The rotor system mass eccentricity which leads to unbalance loads is primarily caused by limitations in the manufacturing process of the rotor system [1]. Large unbalance forces originating from the aeroengine makes the system an active contributor of noise and vibration transfer to the aircraft fuselage. The propagation of vibration energy from the aeroengine to the fuselage significantly affects the wellbeing and comfort of the passengers on board. Minimizing the transfer noise and vibration in the aircraft has gained renowned interest by researchers, seeking advanced active and passive methodologies to minimize vibration transfer in the aircraft by implementing various transfer path analysis (TPA) methods [2–11].
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".