A Novel Method to Predict the Concentricity of Aero-Engine Rotor Considering the Assembly Process of Bolted Flange Joints
Bibliographic record
Abstract
Abstract The concentricity of the aero-engine rotor is an important parameter to evaluate the quality of final assembly and directly affects the vibration characteristics, especially for high-speed rotation. The use of a bolted flange joint is the basic type of connection in aero-engine rotors. During the initial tightening of the bolts, large deformation usually occurs in the flange because its thickness is small. The deformation in the flange has a major impact on the concentricity and consequently the dynamic behavior of the aero-engine rotor. This paper proposes a novel stack-build assembly method to predict the concentricity of multi-stage rotors considering the deformation of the flange. The Small Displacement Torsor (SDT) method is employed to construct the mathematical model of part errors. The homogeneous coordinate transformation method is used to analyze the deviation propagation in the bolted flange joint of each stage part. A finite element (FE) model is built to obtain the deformation of the bolted flange joints by simulating the assembly process. The deformation of the flange is involved in the stack-build assembly model as an error matrix. Furthermore, the influence of the assembly process such as interference and preload, tightening sequence on the concentricity is investigated. The results show that bolted flange joints have a significant effect on concentricity, especially for the complex geometry at the flange interface. The developed approach is validated by experimental tests conducted on a multi-stage rotor. This study can provide guidance and enhance the dynamic performance of bolted joints for 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".