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Record W2990264391 · doi:10.1115/omae2019-95541

New Material Development for Offshore Mooring Chains: High Manganese Steel

2019· article· en· W2990264391 on OpenAlexaboutno aff
Neerav Verma, Andrew Wasson, Zhen Li, Harpreet Sidhar, Haiping He, Hyunwoo Jin, HyunJo Jun, A. Ozekcin, Shiun Ling

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMooringWeldabilitySubmarine pipelineCorrosionManganeseChain (unit)Work (physics)Marine engineeringMaterials scienceEnvironmental scienceMetallurgyEngineeringWeldingMechanical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Oil and gas industry experiences indicate that corrosion and wear of conventional mooring chain is an issue, which can result in costly pre-emptive chain replacement in an offshore environment. There is a need to develop new material technologies with improved performance over conventional carbon steel mooring chains to improve chain reliability. This paper summarizes the development work on one such material — High Manganese Steel (HMS). A version of this steel has been utilized for superior wear performance at oil sands operation in Canada. This paper describes details on HMS chemistry optimization and lab testing for mooring chain application. In addition, Gleeble experiments were carried out to understand HMS hot deformation behavior and weldability. Development testing work on HMS material has shown promising results in terms of corrosion and wear performance, relative to carbon steel mooring chains.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.551
Threshold uncertainty score0.656

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 head0.219
Teacher spread0.207 · 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 designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

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