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Record W2088918333 · doi:10.1243/03093247jsa196

An Engineering Approach for Design and Analysis of Metallic Pipe Joints Under Torsion by the Finite Element Method

2006· article· en· W2088918333 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Strain Analysis for Engineering Design · 2006
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsDalhousie University
FundersKillam Trusts
KeywordsTorsion (gastropod)Finite element methodStructural engineeringParametric statisticsJoint (building)Parametric designSoftwareComputer scienceMaterials scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

Design and stress analysis of pipe joints are still matters of controversy with respect to a unified design approach, despite the fact that many exact and finite element solutions have been presented in the literature. Owing to the complicated and lengthy nature of most exact solutions, development of an applied method for optimized design and stress analysis of a strong and low-cost joint remains a pressing issue. In this work, a simple method was developed for assessing the behaviour of adhesively bonded tubular joints under torsion, based on a parametric study conducted by ABAQUS finite element software. Many case studies of typical metallic joints under torsion were considered for examining the interactions among the main parameters governing the joint performance (i.e. adhesive thickness, pipe and coupling thickness/diameter, joint length, and material properties). Finally, a prototypical joint was designed by using the developed design curves, and the stress distributions were verified by the same software.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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