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Record W1976700572 · doi:10.2118/125129-pa

Submarine Debris Flow Impact on Pipelines: Numerical Modeling of Drag Forces for Mitigation and Control Measures

2009· article· en· W1976700572 on OpenAlexaff
Arash Zakeri, Kaare Høeg, Farrokh Nadim

Bibliographic record

VenueSPE Projects Facilities & Construction · 2009
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsCentre For Cold Ocean Resources Engineering
FundersStatoil
KeywordsFlumeBermDebris flowMarine engineeringDragPipeline transportDebrisSubmarine pipelineEngineeringSeabedGeotechnical engineeringGeologyFlow (mathematics)Aerospace engineeringMechanical engineeringMechanics

Abstract

fetched live from OpenAlex

Summary Provision of mitigative and control measures are necessary for a pipeline to survive in a debris flow event, but the forces in the soil-structure interaction must be estimated for the design. Based on physical experiments in a flume and numerical analyses, this paper presents a method for estimating the impact drag force on laid-on-seafloor and suspended (free-span) pipelines. The method may be applied in practice to a wide range of debris flow impact situations. Two conceptual mitigative and control measures for design against submarine debris flow impact are discussed: the berm-protected laid-on-seafloor pipeline and the cable-controlled pipeline system. The latter may be applied to both the pipeline-on-seafloor and suspended pipeline situations. The observations from a laboratory flume experiment with a model pipe protected by an upstream berm, as well as complementary computational fluid dynamics (CFD) numerical analyses results are presented. The results from the flume experiment show that there is a possibility to protect a pipeline provided the protective structure can withstand the basal shear and lift forces induced by the water and debris flows on its surfaces. The results may be used for conceptual and preliminary design purposes, and the analysis methodology may be tailored to other situations or the detailed design. The feasibility of the two conceptual mitigative and control measures is briefly discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.618

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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