Aero-Engine Vibration Propagation Analysis Using Bond Graph Transfer Path Analysis and Transmissibility Theory
Bibliographic record
Abstract
Abstract In recent times, due to the increase in global energy commodities prices, aero-engine manufacturers are investing in advanced aero-engine technologies to reduce the operating costs. These innovative technologies include overall weight reductions to develop efficient aero-engines. Due to these circumstances, the overall exposure of the aero-engine to vibration transfer due to various loading conditions such as the rotor loading forces has significantly increased. Due to advancement in technologies and demand for greater passenger comfort, vibration transfer reduction to the aircraft fuselage has received prominent attention. In this paper, an analytical transmissibility study called the bond graph Transfer Path Analysis (TPA) has been extensively studied and its applications are explored. Bond Graph TPA is a reliable and feasible theoretical methodology that can be implemented on various large mechanical systems in the design stages to tackle noise and vibration problems before prototyping to significantly reduce the development costs. Bond graph transfer path analysis (TPA) is an advantageous method compared to the existing empirical TPA methodologies such as the Operational Path Analysis due to its efficient analytical nature. In this paper, bond graph TPA has been implemented on a reduced aero-engine model to determine vibration contribution at various aero-engine locations to propose structural design guidelines to minimize the vibration transfer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".