Numerical and Experimental Study of Elastic Interaction in Bolted Flange Joints
Bibliographic record
Abstract
Bolted flange joints are widely used to connect pressure vessels and piping equipment together and facilitate their disassembly. Initial tightening of their bolts is a delicate operation because it is extremely difficult to achieve the target load and uniformity due to elastic interaction. The risk of failure due to leakage and fatigue under service loading is consequently increased. This paper presents a study on the effect of elastic interaction that is present during the tightening of bolted flange joints using three-dimensional nonlinear finite-element modeling and experimentation. The nonlinear nonelastic behavior of the gasket is taken into account in the numerical simulation. The scatter in bolt preload produced during the tightening sequence is evaluated. Based on the elastic interaction coefficient method, the initial target tightening load in each bolt for every pass is determined by using the nonlinear finite-element model to obtain a uniform preload after the final tightening pass. The validity of the finite-element analysis (FEA) is supported by experimental tests conducted on a NPS 4 class 900 weld neck bolted flange joints using fiber and flexible graphite gaskets. This study provides guidance and enhances the safety and reliability of bolted flange joints by minimizing bolt load scatter due to elastic interaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".