Stress Analysis of Bolted Flange Joints With Different Shell Connections
Bibliographic record
Abstract
Abstract Bolted flange joints are widely implemented in various pressure vessel applications of different industries due to their simplicity in installation and operation. However, the most challenging concern during their operation is the level of leakage tightness that they can maintain over time. In addition, the performance of the different flange connection configurations is not well established. Unfortunately, the current ASME BPV Code flange design is not based on a leakage criterion nor a flexibility analysis to give a precise evaluation of the different parameters. This study deals with an evaluation of the integrity and leakage tightness of different types of shells connected to the flange ring. The proposed study is to use the different shell theories to analyze the different parameters such as flange rotation, gasket contact stress and stress distribution at the flange to shell junction. Several types of shell connections, namely cylindrical, spherical, dish and conical are compared. All these types of shells are connected to the raised-face flange ring without the hub. Moreover, to support the analytical approach and validate the study, these shell connections are modelled using a general-purpose finite element software. It is worth noting that, the gasket nonlinear behavior is considered in the FE analysis but not in the analytical modeling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".