Numerical Simulation of the Assembly Process of Bolted Flange Joints Used in Rotating Machinery
Bibliographic record
Abstract
Abstract Bolted joints are widely used to connect structural components in rotating machinery. However, the initial tightening of the bolts is a delicate operation because it is extremely difficult to achieve the target load and uniformity due to elastic interaction. The scatter in the bolt preload has a major impact on the concentricity and consequently the dynamic behavior of rotating machinery. The risk of failure due to vibration and fatigue under service loading becomes an issue. This paper treats the effect of elastic interaction on the eccentricity during the tightening of bolted joints of rotating machinery using finite element (FE) method. In this regard, a two-component bolted flange joint of a high pressure compressor (HPC) of an aero-engine is investigated. The component surface tolerances measured by Rotary Precision Instruments (RPI) are taken into account in the numerical simulation. A method is proposed to calculate the concentricity of components obtained from the radial runout data based on the Least Square method (LSM). The scatter in bolt preload under different interference fit, surfaces tolerance, initial preload, and tightening sequence are evaluated. Furthermore, the influence of these structures and tightening sequence parameters on the concentricity are investigated. The validity of the finite element analysis is supported by experimental tests conducted on scaled specimens of HPC. This study can provide guidance and enhance the dynamic performance of bolted joints for rotating machinery.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".