Analytical Modeling of Flat Face Flanges With Metal-to-Metal Contact Beyond the Bolt Circle
Bibliographic record
Abstract
Design rules for flat face flanges with metal-to-metal contact beyond the bolt circle are covered by Appendix Y of the American Society of Mechanical Engineers Code. These design rules are based on Schneider’s work (1968, “Flat Faces Flanges With Metal-to-Metal Contact Beyond the Bolt Circle,” ASME J. Eng. Power, 90(1), pp. 82–88). The prediction of tightness of these bolted joints relies very much on the level of precision of the self-sealing gasket compression during operation. The evaluation of this compression requires a rigorous flexibility analysis of the joint including bolt-flange elastic interaction. This paper analyses flange separation and the bolt load change in flat face bolted joints. It proposes two different analytical approaches capable of predicting flange rotation and bolt load change during operation. The first method is based on the beam theory applied to a continuous flange sector. This approach is an improvement of the discrete beam theory used in the Schneider model. The second method is based on the circular plate theory and is developed for the purpose of a more accurate assessment of the load changes. As in the Taylor Forge method, this approach is, in general, better suited than the beam theory for flat face flanges, in particular when the flange width is small. The proposed models are compared with the discrete beam theory and validated using numerical finite element analysis on different flange sizes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".