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Record W4405362197 · doi:10.1115/ipc2024-133947

Thermo-Mechanical Analysis of Compression Sleeves

2024· article· en· W4405362197 on OpenAlexaff
Ardeshir Savari, Zach Prestie, Farbod Khayami, Simon S. Park, Ronald J. Hugo

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2024
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCompression (physics)Computer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.236
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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