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Record W2032619903 · doi:10.1177/0954406211421999

Research on pipeline elbow passing for in-pipe robot

2011· article· en· W2032619903 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

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2011
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPipeline (software)RobotMoment (physics)Pipeline transportProcess (computing)MATLABComputer scienceEngineeringAlgorithmSimulationStructural engineeringMechanical engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

In-pipe robots provide inspection and maintenance services to various pipelines. This article proposes an algorithm to calculate the required radial variations for in-pipe robots to pass through pipeline elbows smoothly. It first gives a full overview of a robot passing through a U-shaped elbow and identifies the problem location where the radial dimension changes the most. It then presents a detailed analysis on the focused stage and deduces the algorithm. Based on the obtained algorithm, a realizing Matlab program is written to calculate all possible lengths of front and rear legs at every moment during the process. Finally, the calculation results are presented to precisely describe the track of movement, the length deformations during the whole process, and different contributions of structural variables. This article provides the design and the control of an in-pipe robot with an algorithm to calculate the lengths of its legs at every moment and to what extent the deformations of elastic legs are required.

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)0.006
metaresearch head score (Gemma)0.004
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 score0.301
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.067
GPT teacher head0.305
Teacher spread0.238 · 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