MétaCan
Menu
Back to cohort
Record W2318630826 · doi:10.1515/secm-2012-0171

Energy absorption rate of composite tube as a function of stacking sequence using finite element method

2014· article· en· W2318630826 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

VenueScience and Engineering of Composite Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStackingMaterials scienceIsotropyComposite materialDissipationFinite element methodComposite numberAbsorption (acoustics)Energy (signal processing)Structural engineeringThermodynamicsOpticsMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

Abstract Numerical investigations were performed on a series of laminated composites when subjected to dynamic loadings, in order to determine their energy absorption rate and dissipating energy during impact. The details of numerical modeling, contact analysis, material parameters, and failure criteria were explained and discussed. We aimed to determine the energy absorption rate of various quasi-isotropic lay-ups when subjected to the dynamic loadings, and found that energy dissipation rate varied depending on stacking sequence. In this investigation, we studied how the energy absorption rate and the peak impact load change as a result of changing stacking sequence, with all stacking sequences being quasi-isotropic. Using a constant impacting mass and varied impacting speeds, we found that stacking sequence can significantly control the energy transfer rate or energy absorption rate under dynamic load condition.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.262
Teacher spread0.241 · 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