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Record W4366960790 · doi:10.23977/jemm.2023.080105

Research on transverse impact performance of pipe-in-pipe structure

2023· article· en· W4366960790 on OpenAlexvenueno aff
Xin Hu

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringTube (container)Span (engineering)Transverse planeDisplacement (psychology)Parametric statisticsImpactVibrationMaterials scienceFinite element methodEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Pipe-in-pipe structure are widely used in offshore oil and gas transportation. The structure is subjected to various dynamic loads such as earthquakes and vortex-induced vibrations during service, of which transverse impact is the main cause of structure damage. At present, the whole process analysis and parameter analysis of the pipe-in-pipe structure under the action of impact are not systematic and complete. Based on the explicit dynamic software LS-DYNA, the finite element model of the pipe-in-pipe structure was established, and a systematic parametric analysis was carried out on the parameters of the pipe-in-pipe structure. Based on the three aspects of force, displacement and energy absorption, the influence of the three parameters of span, diameter ratio and thickness ratio on the impact resistance of the tube-in-tube structure is revealed. The research results show that the impact force time-history curve of the pipe-in-pipe structure can be divided into five stages. The resistance of the tube-in-tube structure to impact is significantly influenced by the ratio of thickness and span.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.282
Teacher spread0.264 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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