Thermo-Mechanical Analysis of Compression Sleeves
Bibliographic record
Abstract
Abstract In recent years, metallic compression sleeves have gained prominence due to their role in enhancing the integrity of pipeline systems. These sleeves can potentially extend the operational life of pipelines by mitigating crack propagation and strengthening material properties. They offer an economically viable repair method, reducing operational disruptions and risks associated with installation, especially under challenging operating conditions. While preliminary studies have been promising, a more detailed understanding of stress dynamics during both the installation process and subsequent operation of the sleeves is needed. This research aims to conduct thermo-mechanical evaluations of Type-A compression sleeves, which are notable for their absence of end fillet welds. We utilize finite element (FE) analysis with the intention of providing insight into the reliability assessment of these systems. The current study employs a coupled field transient system, accommodating the simultaneous application of mechanical variables, such as internal pressure, and thermal factors due to sleeve preheating and welding. This approach mirrors the steps of sleeve installation, from changes in operating pressure and induction heating to welding and cooling. Moreover, the study examines the performance of compression sleeves through a sensitivity anlaysis of significant parameters including internal pressure, mechanical properties of epoxy resin, tolerance fit, and contact between pipe, epoxy resin, and compression sleeve. This study holds significant promise in guiding the compression sleeve application procedures, structural design, and material choices, culminating in enhanced structural robustness and dependability of pipe systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".