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Effect of Backfill Temperatures on Axial Restraint of Pipelines

2021· article· en· W3153393315 on OpenAlexaff
Jim Oswell, Jeff Nicholls, Geoff Meronek, Peter Sombyk

Bibliographic record

VenueJournal of Pipeline Systems Engineering and Practice · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsTransCanada (Canada)Safe Engineering Services & Technologies (Canada)
Fundersnot available
KeywordsPipeline transportPipeline (software)Environmental scienceGeotechnical engineeringStrain gaugeAir temperaturePetroleum engineeringDeltaGeologyEngineeringEnvironmental engineeringStructural engineeringAtmospheric sciencesAerospace engineeringMechanical engineering

Abstract

fetched live from OpenAlex

One consideration for pipeline design is the Delta-T. There may be a tendency to assume a conservative value for winter construction, the extreme value being the air temperature. In applying a low restraint temperature in winter, the Delta-T is increased. The consequence of an artificially high Delta-T is that the pipeline may notionally experience high stresses; in extreme cases, mitigation is required to counteract these stresses. In winter, the soil at pipeline depth will usually be unfrozen or slightly frozen. When a pipeline nominally at ambient air temperature is backfilled, the warmer backfill soil will quickly warm the pipeline steel from the air temperature to a more moderate temperature. As a result, the pipeline is unlikely to attain full axial restraint at low air temperatures but instead at more temperate temperatures. In contrast, for pipelines constructed in summer and assumed to be backfilled at warm air temperatures, the resulting Delta-T may be nonconservative, particularly for hot pipelines. This paper presents the test results for several pipelines installed in winter and summer. The pipelines were instrumented with thermistors to monitor pipeline and soil temperatures and strain gauges to monitor pipeline strains during the backfilling process. Guidance is provided as to appropriate tie-in temperatures.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.239
Teacher spread0.234 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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