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Record W1975712130 · doi:10.1115/1.4023431

Framework for a Combined Netting Analysis and Tsai-Wu-Based Design Approach for Braided and Filament-Wound Composites

2013· article· en· W1975712130 on OpenAlexaff
Rifat Hossain, Jason P. Carey, Pierre Mertiny

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNettingPipingStructural engineeringFiberMaterials scienceComposite numberFilament windingComposite materialUltimate tensile strengthMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Axially symmetric fiber-reinforced polymer composite structures, such as pressure vessels and piping, are being widely used in different industrial applications where combined loading conditions may be applied. It is imperative to determine a suitable fiber angle, or a distribution of fiber angles, along the longitudinal direction of the structure in order to achieve best performance in terms of mechanical behavior and strength for structures subjected to combined loadings. To this end, an approach combining netting analysis and Tsai-Wu failure theory was employed as a design tool to assess critical fiber angles at which applied loadings would cause a structure to fail. Together, the proposed netting analysis and failure theory-based approach constitute a simple, expedient, and convenient design process for complex-shaped structures.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.057
GPT teacher head0.318
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations8
Published2013
Admission routes1
Has abstractyes

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