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Record W2608591069 · doi:10.1177/0731684417707585

Flow-control and hybridization strategies for thermoplastic stiffened panels of long discontinuous fibers

2017· article· en· W2608591069 on OpenAlexaff
Swaroop B Visweswaraiah, Nicolas Ohlmann, Larry Lessard, Pascal Hubert

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsMcGill University
Fundersnot available
KeywordsWavinessMaterials scienceFlangeFinite element methodComposite materialReinforcementDelamination (geology)Flow (mathematics)Flow control (data)Structural engineeringComputer scienceEngineeringMechanics

Abstract

fetched live from OpenAlex

The current research aims at mitigating the flow-induced manufacturing issues (strand waviness and swirling of strands) encountered in complex parts of randomly oriented strands, through the hybridization of randomly oriented strands with continuous fibers, while emphasizing the ease of manufacturing and repeatability. Three hybridization strategies are proposed for T-stiffeners that represent the generalized intersecting junctions of stiffened panels. The strategies include: flow-control element, flange reinforcements, and rib reinforcements. A quantitative assessment of pull-out strengths of five T-stiffener configurations is made. Flow-control element improves the strand flow at the junction, reduces variability, and enhances the pull-out b-basis design allowable by about 24%. A quasi-isotropic laminate as flange reinforcement with a flow-control element produces 12.5% pull-out strength improvement. Rib reinforcement causes reinforcement delamination from the randomly oriented strands part, dropping pull-out strength by about 6%. A transient heat transfer analysis of the tooling set-up was simulated using finite elements to devise a preferential cooling strategy that minimizes porosity in randomly oriented strands panels with T-stiffeners.

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 score0.651
Threshold uncertainty score0.513

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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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