MétaCan
Menu
Back to cohort
Record W4386776822 · doi:10.3390/modelling4030023

Investigating Ice Loads on Subsea Pipelines with Cohesive Zone Model in Abaqus

2023· article· en· W4386776822 on OpenAlex

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

VenueModelling—International Open Access Journal of Modelling in Engineering Science · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsCentre For Cold Ocean Resources EngineeringMemorial University of Newfoundland
Fundersnot available
KeywordsSubseaIcebergFracture (geology)GeologyPipeline transportStructural engineeringMechanicsPosition (finance)Geotechnical engineeringEngineeringSea icePhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Subsea pipelines and cables placed in ice-prone regions may be at risk of iceberg damage. In particular, pipes that are not buried may come in direct contact with iceberg keels. Knowing the range of interaction forces helps to assess the types and magnitudes of potential damage. Experimental studies provide the most valuable data about the interaction forces, while numerical modeling may give insight into configurations that are difficult to study experimentally. This work applies the cohesive zone model to investigate the fracture behavior of ice samples. Simulations are performed in 2D with Abaqus explicit solver. Modeled interaction forces from multiple simulations are recorded and compared to understand how the geometry of the samples affects the fracture. Repeat interactions with different grain configurations are conducted to investigate associated variance in fracture patterns and loads. t-tests show that the force application angle and the indenter’s position significantly affect the fracture force.

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)0.002
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0030.000
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 head0.070
GPT teacher head0.328
Teacher spread0.258 · 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