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Record W4385887729 · doi:10.55274/r0011356

L51647 Welding on Fluid Filled and Pressurized Pipelines-Transient 3D Analysis

2000· report· en· W4385887729 on OpenAlex
John Goldak

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsGroove (engineering)WeldingInternal pressureFinite element methodMaterials sciencePipeline transportConvectionStructural engineeringMetallurgyComposite materialMechanicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The objective of this project was to determine if research in Computational Weld Mechanics had matured to the stage where it could simulate the process of welding on a pressurized pipeline and provide useful estimates of the risk of burn-through. To achieve that objective we have compared the results of our FEM analyzes of several welds with the experimental data reported in "http://www.prci.com/publications/L51763.htm" PR-185-9515, Repair of Pipelines by Direct Deposition of Weld Metal: Further Studies. The temperature and deformation predicted by our FEM analysis agrees quite well with the experimental data. The critical input data in addition to the internal pressure in the pipe, the geometry of the pipe, the size and shape of the weld pool including weld reinforcement, are the convection coefficient on the internal pipe surface and the temperature dependence of the viscosity of the pipe metal. Our FEM analysis shows that creep under the weld pool can thin the pipe wall and form a groove. In welds that show significant groove formation and thus high risk of burn through, this groove is significantly deeper than in welds that are at low risk of burn-through. When the pipe wall is thinned by the groove, the internal pipe wall temperature increases under the weld pool. Also the groove could reduce the convection on the internal pipe wall. This would further increase the temperature on the internal pipe wall under the weld pool and further accelerate actual burn-through. In our FEM analysis, we found no significant groove formation in those welds for which no significant groove formation was reported in the PR-185-9515 experiments. We found significant groove formation exactly in those welds that burned-through or were at high risk of burn-through. In those welds, the FEM analyses predicted a somewhat deeper groove than experiment. This suggests the FEM analyses erred on the safe side. In this sense, we conclude that we have succeeded in computing useful estimates of the risk of burn-through using Computational Weld Mechanics. It is notable that almost no use is made of adjustable or tuning parameters. To simulate the actual burn-through we conjecture that we would need to include inertial forces in the stress analysis.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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