Effect of the Bending Stiffness of Bolted Flange Joints on the Dynamics of Aero-Engine Rotors
Bibliographic record
Abstract
Abstract Bolted flange joints are used to connect adjacent disks and drums in aero-engine rotors. The flange mating contact surfaces usually move relative to each other and deform when they are subjected to numerous combinations of loads during normal operation. Consequently, the local bending stiffness of the bolted flange joint is altered considerably affecting the overall rotor stiffness and therefore the rotor dynamics. This paper presents a study on the effect of the bending stiffness of bolted flange joints on the dynamic characteristics of an aero-engine rotor. A method to calculate the bending stiffness is proposed using an equivalent model based on an axial spring and bending beam stiffnesses. The relationship of the structure geometry, loading and assembly conditions of the bolted flange joints, and the bending stiffness loss are obtained. Furthermore, a dynamic model of an aero-engine high-pressure compressor rotor is developed using the transfer matrix method that incorporates the bending stiffness effect of the bolted flange joint. Based on this model, the influence of bending stiffness on the natural frequency and the steady-state response is discussed. The results show that the stiffness loss of bolted flange joint has a significant impact on the rotor dynamics. The structure geometry, loading, and assembly conditions can be used to control the dynamic response of aero-engine rotors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".