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Record W3197375248 · doi:10.1139/cgj-2021-0089

Evaluation of horizontal submarine slide impact force on pipeline via a modified hybrid geotechnical – fluid dynamics framework

2021· article· en· W3197375248 on OpenAlexvenueno aff
Ning Fan, Wangcheng Zhang, Fauzan Sahdi, Tingkai Nian

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsSubmarine pipelineSeabedPipeline (software)Pipeline transportGeotechnical engineeringMarine engineeringGeologyOffshore geotechnical engineeringEngineeringPetroleum engineeringMechanical engineering

Abstract

fetched live from OpenAlex

There are situations in offshore energy development where potential impact forces between submarine slides and pipelines need to be estimated. The horizontal slide – pipeline impact force, parallel to the main travel direction of the sliding mass and normal to the pipeline axis, is generally dominant compared with other force components and hence of particular concern. In practice, pipelines may be suspended at varying distances above the seabed (gap) and existing methods do not consider how this will affect the horizontal slide – pipeline forces. This paper investigates the effects of pipeline–seabed gap and pipeline diameter on the horizontal slide – pipeline impact force via 181 computational fluid dynamics (CFD) simulations at Reynolds numbers of 0.36–287. Results show that variation in the pipeline–seabed gap and pipeline diameter alters the slide mass flow behaviour as it flows past the pipeline and hence the impact force when the pipeline–seabed gap is below a critical value. A modified hybrid geotechnical – fluid dynamics framework for estimating the horizontal impact force is proposed by considering the effects of the pipeline–seabed gap and pipeline diameter, which is validated with existing experimental datasets.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.011
GPT teacher head0.238
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.

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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

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