Vibration Transfer Path Analysis of Aeroengines Using Bond Graph Theory
Bibliographic record
Abstract
Increase in energy commodity prices worldwide has forced aeroengine developers to propose advanced innovative design technologies to significantly reduce the operating costs by proposing lightweight and efficient aeroengines. This increases the overall sensitivity of the aeroengine to internal excitations and vibrations, which significantly impacts the safety of the crew and passengers. In this chapter, a reliable analytical transmissibility scheme and protocol called transfer path analysis (TPA) is implemented in conjunction with bond graph methodology to perform vibration propagation analysis throughout the aeroengine structure to tackle noise and vibrations issues. To assess vibration propagation in the aeroengine, a reduced aeroengine model is proposed. Using the proposed aeroengine model, the bond graph representation of the aeroengine is developed. Thereafter, by implementing the outlined methodology and theory, the characteristic governing dynamic equations of motion of the aeroengine are obtained. Using the theory of global transmissibility, the transmissibility between various inertia elements in the aeroengine are determined. Using the obtained transmissibilities, vibration energy propagation for various paths in the aeroengine are analyzed. Thereafter, vibration reduction guidelines are proposed based on possible structural modifications aimed at minimizing vibration energy transfer from the aeroengine to the aircraft fuselage. This work has also shown that the proposed bond graph TPA method can be applied during the design and development stage, which can significantly reduce the development costs as no actual prototyping is required.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".