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Record W2767086998 · doi:10.1115/pvp2017-65523

Deformation Analysis of Dented Pipeline via Surface Interpolation

2017· article· en· W2767086998 on OpenAlexaff
Chike Okoloekwe, Muntaseer Kainat, Doug Langer, Sherif Hassanien, J. J. Roger Cheng, Samer Adeeb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFinite element methodInterpolation (computer graphics)Pipeline (software)Displacement (psychology)Spline (mechanical)Shell (structure)Deformation (meteorology)Nonlinear systemPipeline transportStructural engineeringCoordinate systemComputer scienceGeometryMathematical analysisMathematicsEngineeringGeologyMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Advances in the interpolation techniques of discrete data points and its application to monitoring the displacement of physical infrastructure has led to improved analytical strain evaluation procedures. In order to generate a detailed mathematical model of the strain state of a dented pipeline, it is necessary to decompose the deformation data obtained from monitoring devices into the corresponding radial, longitudinal and circumferential components. In this paper, a technique for analytically evaluating the strains in dented pipelines based on the coordinates of the geometric profile of the dent is investigated and the strains predicted from the said method are benchmarked against the strains predicted from a numerical model generated using nonlinear finite element analysis (FEA) and the codified equations for evaluating strains in dented pipes. This novel technique to strain analysis is an application of the principles of shell theory to a deformed pipeline in order to evaluate the components of the displacements in the cylindrical coordinate system. The coordinates of the deformed profile are obtained from the FEA model and interpolated with B-Splines curves equipped with second order continuity. The resulting strain distribution along the thickness of the pipe wall is evaluated analytically by performing derivatives on the spline functions. The good agreement obtained in the strains predicted by our model and FEA indicates a possibility of conducting in-depth strain analysis of thin-walled structures without the need for the rigorous FEA.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.239

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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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