A Novel Methodology to Optimize the Tightening Sequence in Bolted Flange Joints
Bibliographic record
Abstract
Abstract Bolted flange joints are the most complex structural components of pressure vessels and piping equipment. Their assembly is a delicate task that determines their successful operation during the service life. During bolt tightening, it is very difficult to achieve uniformity of the target bolt preload due to elastic interaction and criss-cross talk. The risk of leakage failure under service loading is consequently increased because of the scatter of the bolt preload. In previous work, an analytical model based on the theory of circular beams on linear elastic foundation was proposed to predict the bolt tension change due to elastic interaction. Based on this model, this paper presents a novel methodology for the optimization of the tightening sequence. The target preload and the load to be applied to each bolt in each pass can be calculated to achieve uniform final preload and avoid bolt tension reaching yield under a number of specified tightening passes. The validity of the approach is supported by experimental tests conducted on a NPS 4 class 900 welding neck flange joint and by finite element analysis on this bolted joint using the criss-cross tightening and sequential patterns. This study provides guidelines for bolted flange joints assembly and enhances its safety and reliability by minimizing bolt tension scatter due to elastic interaction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".