A Novel Optimization Method to Improve Assembly Concentricity of Aero-Engine Rotor With Bolted Flange Joints Using Genetic Algorithms
Bibliographic record
Abstract
Abstract The assembly concentricity of the aero-engine rotor is a crucial parameter for assessing the overall assembly quality. It significantly influences vibration characteristics, particularly during high-speed rotation. Aero-engine rotors commonly utilize bolted flange joints as the fundamental method of connection. However, it is very difficult to accurately control the concentricity in practice due to the influences of geometric errors, assembly angles, et al. This paper presents a novel method to optimize the assembly concentricity of multi-stage aero-engine rotors using a genetic algorithm, which considers the amount of concentricity of each rotor. The objective of the optimization is to minimize the concentricity. The assembly angles of the bolted flange joints rotors are considered as the design variables. A prediction model for the concentricity of multi-stage rotors related to the assembly angles is proposed by using the homogeneous coordinate transformation theory. The concentricity of the final assembly is minimized by controlling the assembly angle of each rotor and the best assembly angles of different rotors can be obtained. The developed approach is validated by experimental tests 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".