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Record W2528535548 · doi:10.1504/ijmsi.2016.079646

Random fatigue analysis of drill-pipe threaded connection

2016· article· en· W2528535548 on OpenAlexaff
Jiahao Zheng, Jianming Yang

Bibliographic record

VenueInternational Journal of Materials and Structural Integrity · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsStructural engineeringDrill pipeDrill stringDrillDrillingRandom vibrationCable glandModal analysisStress (linguistics)VibrationVibration fatigueConnection (principal bundle)EngineeringMaterials scienceComputer scienceFinite element methodMechanical engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

Threaded connections are widely used in connecting drill pipes into a drill string. During drilling operations, the threaded connections are subjected to various external loads, including the load from the unevenness of rock formation at the bottom, impact from the well bore and axial hook load etc. These loads are primarily random, and will cause fatigue damage to the connections during drilling operation. In this paper, a standard 4.5" API line pipe threaded connection is analysed to investigate the fatigue effect of the random loads. A static stress analysis is first conducted considering 'make-up' and 'tensile load' steps. Then modal analysis and random vibration analysis are carried out, with the excitation considered as random. The fatigue damage is predicted using the so called three-band technique based on the stress results obtained. Effects of parameters, such as the random excitation, location of the connection, and friction coefficient, on the fatigue are 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.830

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)0.0010.000

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 head0.025
GPT teacher head0.280
Teacher spread0.254 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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