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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 OpenAlexaff
Ramin Amid, Zouheir Fawaz, Hamid Ghaemi

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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

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 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

Citations3
Published2014
Admission routes1
Has abstractyes

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