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Record W2072248753 · doi:10.1115/omae2014-23503

Determination of Wave Impact Loads for the Hebron Gravity Based Structure (GBS)

2014· article· en· W2072248753 on OpenAlexaffabout
Robert Oberlies, Jameel Khalifa, Jerry Huang, Steinar Hetland, Adel Younan, Meghan Overstake, Scott Slocum

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsExxonMobil (Canada)
Fundersnot available
KeywordsSubmarine pipelineEngineeringTerm (time)SalientStructural engineeringStormMarine engineeringGeologyGeotechnical engineeringComputer sciencePhysicsOceanography

Abstract

fetched live from OpenAlex

ExxonMobil Canada Properties and its co-venturers are building a gravity based structure (GBS) in Newfoundland and Labrador to be installed on the Hebron Field offshore Eastern Canada. This area is characterized by harsh storms with large waves and high winds. The geometry of the Hebron GBS has an effect on the behavior of the incident waves with regards to their likelihood of breaking onto the shaft. Model tests of the structure in storm waves were executed to provide local wave impact load data on the shaft of the GBS. These tests required significant planning and design of the model, environment, and instrumentation in order to properly satisfy the test objectives. The results of the test showed that the measured wave impact loads on the structure were highly variable, requiring a long-term, response based method to quantify the design loads on an annual exceedance basis. In this paper, we discuss the salient aspects of the model testing effort and the long-term analysis approach which was utilized to define the wave impact loads that were incorporated into the Hebron GBS structural design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.871
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2014
Admission routes2
Has abstractyes

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